SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks | Paper Citation Record

Observation 86a552eb-5555-4bcb-92bd-e3d455297d2e · outbound

This paper cites GPT-4 Technical Report.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks GPT-4 Technical Report

Reference 1

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Observation b3996b3e-4ecd-4d39-8a70-888d02de4195 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 2

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Observation 2921a5e6-7932-4a7a-9ba7-18db271a7373 · outbound

This paper cites PaLM 2 Technical Report.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks PaLM 2 Technical Report

Reference 3

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Observation f74fdfd4-d04b-4f4a-bbd4-a3360bcc7369 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 4

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Observation fff49982-1dee-48bb-87ff-2648c24c3c79 · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks The claude 3 model family: Opus, sonnet, haiku

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.671788Z

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Observation edbf5da5-8f47-4fcd-8d18-9aa63fc13db6 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.676859Z

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Observation e308f6eb-d70d-41f1-9912-3914d2dec667 · outbound

This paper cites Fine-tuning a llm using reinforcement learning from human feedback for a therapy chatbot application, 2023.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Fine-tuning a llm using reinforcement learning from human feedback for a therapy chatbot application, 2023

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.682539Z

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Observation b09f5654-e292-42dc-b38a-8ac18b1a53c1 · outbound

This paper cites Towards next-generation intelligent assistants leveraging llm techniques.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Towards next-generation intelligent assistants leveraging llm techniques

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.688244Z

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Observation 22cfa1f3-d4d2-4ecf-be69-0e183563783b · outbound

This paper cites Bing chat: The future of search engines?Proceedings of the Association for Information Science and Technology, 60(1):1007–1009, 2023.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Bing chat: The future of search engines?Proceedings of the Association for Information Science and Technology, 60(1):1007–1009, 2023

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.692938Z

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Observation df9a6b82-f1a5-4fcd-8c5d-6e68b03f473f · outbound

This paper cites Large language models encode clinical knowledge.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Large language models encode clinical knowledge

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.698453Z

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Observation 51404f11-e516-42b7-9280-1f7e8f7ec994 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Parameter-Efficient Transfer Learning for NLP

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.703451Z

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Observation d6f0f194-6c77-4993-83ce-9efc21516914 · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.708586Z

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Observation cdbb867e-45d0-4c1c-bcaf-f24b17dc5f75 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.714732Z

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Observation 9abad158-d022-4efb-8dad-27666eccbb6c · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.720923Z

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Observation 6dc9630a-b4af-419a-96ae-a748dd0d314e · outbound

This paper cites BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models, September 2022.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models, September 2022

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.726046Z

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Observation 5b869867-aa96-4251-af8d-520b3358767e · outbound

This paper cites Parameter-Efficient Fine-Tuning without Introducing New Latency.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Parameter-Efficient Fine-Tuning without Introducing New Latency

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.731208Z

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Observation ca192346-8e46-4a84-a7b6-2485ae9a4ea1 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.Iclr, 1(2):3, 2022.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks LoRA: Low-rank adaptation of large language models.Iclr, 1(2):3, 2022

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.737824Z

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Observation 71c8732c-3d62-45c6-87d0-e4520880e56d · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.743884Z

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Observation dea194a5-e9dd-40f7-b221-5877e7d1de89 · outbound

This paper cites HydraLoRA: An Asymmetric LoRA architecture for efficient fine-tuning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks HydraLoRA: An Asymmetric LoRA architecture for efficient fine-tuning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.912584Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation db84a42d-39ac-44f4-baab-7fbab9ba8aab · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.765402Z

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Observation 1dac6935-d354-4d14-87b5-ccde5fb99fb5 · outbound

This paper cites DyLoRA: Parameter-efficient tuning of pre-trained models using dynamic search-free low-rank adaptation.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks DyLoRA: Parameter-efficient tuning of pre-trained models using dynamic search-free low-rank adaptation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.882437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 51a56941-d80f-4b7f-86b4-13b5ed29e91e · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehen- sive Survey.Transactions on Machine Learning Research, October 2024.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Parameter-Efficient Fine-Tuning for Large Models: A Comprehen- sive Survey.Transactions on Machine Learning Research, October 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.865649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 39beeda1-b1bf-4e69-902e-7dbd5f2e59d2 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Communication-efficient learning of deep networks from decentralized data

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.779595Z

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Observation fe098628-78b7-49a7-b9cb-b23e21b906ce · outbound

This paper cites Advances and open problems in federated learning.Foundations and trends in machine learning, 14(1-2):1–210, 2021.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Advances and open problems in federated learning.Foundations and trends in machine learning, 14(1-2):1–210, 2021

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.785017Z

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Observation cf631879-5429-498d-84a0-e64c9389fbdc · outbound

This paper cites Federated Large Language Models: Feasibility, Robustness, Security and Future Directions.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Federated Large Language Models: Feasibility, Robustness, Security and Future Directions

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.790114Z

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Observation 9404c3c7-2117-46b3-8bc7-ec9f23f13668 · outbound

This paper cites A survey on federated learning systems: Vision, hype and reality for data privacy and protection.IEEE Transactions on Knowledge and Data Engineering, 35(4):3347–3366, 2021.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks A survey on federated learning systems: Vision, hype and reality for data privacy and protection.IEEE Transactions on Knowledge and Data Engineering, 35(4):3347–3366, 2021

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.827828Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1320001b-1327-41cb-9d58-bcbaacdfdc9d · outbound

This paper cites Efficient federated learning for modern NLP.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Efficient federated learning for modern NLP

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.811089Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0cc0e0da-3b62-435e-926b-10d2c3d0fd7f · outbound

This paper cites Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.806033Z

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Observation 4c3d86f6-3b02-46d6-982f-13cba7b4de93 · outbound

This paper cites Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Fedprompt: Communication-efficient and privacy-preserving prompt tuning in federated learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.794157Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d7d04d5d-2318-4903-87ba-54677332640e · outbound

This paper cites Text-driven Prompt Generation for Vision-Language Models in Federated Learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Text-driven Prompt Generation for Vision-Language Models in Federated Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.816374Z

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Observation 403d99ee-8fc8-4c0d-8e88-c5793100199a · outbound

This paper cites Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.821293Z

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Observation fda0baeb-b061-4060-9b7b-4e187f9a5fff · outbound

This paper cites Rethinking LoRA for data heterogeneous federated learning: Subspace and state alignment.arXiv preprint arXiv:2602.01746, 2026.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Rethinking LoRA for data heterogeneous federated learning: Subspace and state alignment.arXiv preprint arXiv:2602.01746, 2026

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.826438Z

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Observation b5aafe2a-4524-4c00-90b4-81ddf0017202 · outbound

This paper cites LoRA+: Efficient low rank adaptation of large models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks LoRA+: Efficient low rank adaptation of large models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.778151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 841df353-edf8-4791-9571-1f60e05d6b37 · outbound

This paper cites DoRA: Weight-decomposed low-rank adaptation.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks DoRA: Weight-decomposed low-rank adaptation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.762254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca56a59a-ab58-467d-888c-820fa3ec3c73 · outbound

This paper cites LoRA-drop: Efficient LoRA parameter pruning based on output evaluation.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks LoRA-drop: Efficient LoRA parameter pruning based on output evaluation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.745991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 195876e1-6cb5-4c60-99c9-5cb88e37430b · outbound

This paper cites LLaMA-LoRA neural prompt engineering: A deep tuning framework for automatically generating chinese text logical reasoning thinking chains.Data Intelligence, 2024.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks LLaMA-LoRA neural prompt engineering: A deep tuning framework for automatically generating chinese text logical reasoning thinking chains.Data Intelligence, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.726938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4c687631-ed21-4191-b408-e277f6f14cdf · outbound

This paper cites LoRA training provably converges to a low-rank global minimum or it fails loudly (but it probably won’t fail).

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks LoRA training provably converges to a low-rank global minimum or it fails loudly (but it probably won’t fail)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.710817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0a25ae94-eab1-46be-bf6e-85ab90250a2c · outbound

This paper cites Towards building the federated GPT: Federated instruction tuning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Towards building the federated GPT: Federated instruction tuning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.692854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7da8ca57-455b-40e8-aa1b-cf92b30fcbf9 · outbound

This paper cites Flexlora: Entropy-guided flexible low-rank adaptation.arXiv preprint arXiv:2601.22905, 2026.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Flexlora: Entropy-guided flexible low-rank adaptation.arXiv preprint arXiv:2601.22905, 2026

Reference 39

Resolution: verified exact; raw_fallback, observed 2026-08-14T04:16:49.824744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5ff77877-8eb1-44a7-9e88-51f8371cdc5d · outbound

This paper cites Rethinking loRA for privacy-preserving federated learning in large models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Rethinking loRA for privacy-preserving federated learning in large models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.675124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 209fce20-2346-45f2-9fdf-02b0bee59c8e · outbound

This paper cites Heterogeneous LoRA for federated fine-tuning of on-device foundation models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Heterogeneous LoRA for federated fine-tuning of on-device foundation models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.657062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 60885078-b15b-4880-9191-3b7862665f4e · outbound

This paper cites Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.876234Z

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Observation 1123cccf-2ec4-4a25-9e14-5255c008a290 · outbound

This paper cites Selective aggregation for low-rank adaptation in federated learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Selective aggregation for low-rank adaptation in federated learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.640430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation df5caaa9-e22d-4120-912f-0d98c118a65b · outbound

This paper cites Improving loRA in privacy-preserving federated learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Improving loRA in privacy-preserving federated learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.623057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 496851cc-6492-424f-9a40-9e5a8cd582fd · outbound

This paper cites FLoRA: Federated fine-tuning large language models with heterogeneous low-rank adaptations.Advances in Neural Information Processing Systems, 37:22513–22533, 2024.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FLoRA: Federated fine-tuning large language models with heterogeneous low-rank adaptations.Advances in Neural Information Processing Systems, 37:22513–22533, 2024

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.606921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dc27fd91-e11c-4ee1-85fa-1e7cbaf89e18 · outbound

This paper cites Federated fine-tuning of large language models under heterogeneous tasks and client resources.Advances in Neural Information Processing Systems, 37:14457–14483, 2024.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Federated fine-tuning of large language models under heterogeneous tasks and client resources.Advances in Neural Information Processing Systems, 37:14457–14483, 2024

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.897533Z

Source-reported events for the cited work

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Observation b9a0c3bd-078f-4a6c-a08d-614efa148746 · outbound

This paper cites Federated sketching LoRA: On-device collaborative fine-tuning of large language models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Federated sketching LoRA: On-device collaborative fine-tuning of large language models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.580420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9391a5fd-fafe-4e8e-99f6-8c00730f1974 · outbound

This paper cites Practical sketching algorithms for low-rank matrix approximation.SIAM Journal on Matrix Analysis and Applications, 38(4):1454–1485, 2017.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Practical sketching algorithms for low-rank matrix approximation.SIAM Journal on Matrix Analysis and Applications, 38(4):1454–1485, 2017

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.562102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 77c0f445-5c86-49ee-8003-508a762ab1e0 · outbound

This paper cites Towards federated low-rank adaptation of language models with rank hetero- geneity.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Towards federated low-rank adaptation of language models with rank hetero- geneity

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.545522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c067bfcf-c9d3-4dd9-b60f-4efdf66cc504 · outbound

This paper cites RB-LoRA: Rank-balanced aggregation for low-rank adaptation with federated fine-tuning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks RB-LoRA: Rank-balanced aggregation for low-rank adaptation with federated fine-tuning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.529936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 82035104-a4f5-42a4-84a2-bdc460dd96e7 · outbound

This paper cites Fedsrd: Sparsify-reconstruct- decompose for communication-efficient federated large language models fine-tuning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Fedsrd: Sparsify-reconstruct- decompose for communication-efficient federated large language models fine-tuning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.515095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 136a142d-11b5-48fc-8a91-a1ad39e37c98 · outbound

This paper cites Adaptive rank allocation for federated parameter-efficient fine-tuning of language models.IEEE Transactions on Computers, 2026.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Adaptive rank allocation for federated parameter-efficient fine-tuning of language models.IEEE Transactions on Computers, 2026

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.500283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 19a918ec-8ced-4e1e-8075-3e444e505ed1 · outbound

This paper cites Sparse low-rank adaptation of pre-trained language models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Sparse low-rank adaptation of pre-trained language models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.932718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c693a8ca-06d4-4708-bea3-d973277f2e62 · outbound

This paper cites Robust federated finetuning of LLMs via alternating optimization of LoRA.Advances in Neural Information Processing Systems, 38:120038–120090, 2026.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Robust federated finetuning of LLMs via alternating optimization of LoRA.Advances in Neural Information Processing Systems, 38:120038–120090, 2026

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.472671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aa8d87dc-f5d1-4d32-8a00-9feb22b6ef0c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.944043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fd3394d-1a4a-4873-8cdb-07801712dd3e · outbound

This paper cites GLUE: A multi-task benchmark and analysis platform for natural language understanding.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks GLUE: A multi-task benchmark and analysis platform for natural language understanding

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.457311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b963033a-7efa-460f-970e-79344db4b548 · outbound

This paper cites Federated learning for open banking.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Federated learning for open banking

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.442717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e5f312f8-c525-4375-ab9d-7efdee0feace · outbound

This paper cites an unresolved cited work.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-14T04:16:50.427924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 567e2a8d-3c69-40a0-8320-6178f77b13fb · outbound

This paper cites Specificity-preserving federated learning for mr image reconstruction.IEEE Transactions on Medical Imaging, 42(7):2010–2021, 2022.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Specificity-preserving federated learning for mr image reconstruction.IEEE Transactions on Medical Imaging, 42(7):2010–2021, 2022

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.412060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 94910313-68e2-401e-b51c-1bd5554ca0a3 · outbound

This paper cites Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, and Ziyue Xu.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, and Ziyue Xu

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.397047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c0110c60-92dc-47b4-933b-d811fa6eadc6 · outbound

This paper cites Learning federated visual prompt in null space for mri reconstruction.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Learning federated visual prompt in null space for mri reconstruction

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.381805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b3cdf7d5-3eb0-42eb-a3b3-b1ef532fff79 · outbound

This paper cites Cross-modal vertical federated learning for mri reconstruction.IEEE Journal of Biomedical and Health Informatics, 2024.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Cross-modal vertical federated learning for mri reconstruction.IEEE Journal of Biomedical and Health Informatics, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.366548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7ddbab07-77c6-4200-a17f-cdc750ae91ed · outbound

This paper cites Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:16:49.710294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5651fa05-28bc-498a-971a-3eba652e4da9 · outbound

This paper cites FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:48.988150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b51067df-2a38-4bcf-8d87-6ae17488afc7 · outbound

This paper cites FederatedScope-LLM: A comprehensive package for fine-tuning large language models in federated learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FederatedScope-LLM: A comprehensive package for fine-tuning large language models in federated learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.352376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 91b60dd1-247f-45a7-9943-fb63ca631069 · outbound

This paper cites OpenFedLLM: Training large language models on decentralized private data via federated learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks OpenFedLLM: Training large language models on decentralized private data via federated learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.337400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 05d9897a-5a82-4ed0-bd8e-42e12a1fc046 · outbound

This paper cites FedLLM- Bench: Realistic benchmarks for federated learning of large language models.Advances in Neural Information Processing Systems, 37:111106–111130, 2024.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FedLLM- Bench: Realistic benchmarks for federated learning of large language models.Advances in Neural Information Processing Systems, 37:111106–111130, 2024

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.219573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 35b26f49-ef11-4b1b-bc01-a92d4ce935a5 · outbound

This paper cites Fed- BCGD: Communication-efficient accelerated block coordinate gradient descent for federated learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Fed- BCGD: Communication-efficient accelerated block coordinate gradient descent for federated learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.203185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6f5f60c5-89b1-4427-aa63-3183f86fbf06 · outbound

This paper cites Improving generalization in federated learning with highly heterogeneous data via momentum-based stochastic controlled weight averaging.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Improving generalization in federated learning with highly heterogeneous data via momentum-based stochastic controlled weight averaging

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.187145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 51ac086c-f01e-48d4-a183-a25a2f68d975 · outbound

This paper cites FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:49.020967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation facea6ef-1220-4b48-acc7-9f308b8847da · outbound

This paper cites Consistency of local and global flatness for federated learning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Consistency of local and global flatness for federated learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.168353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1fdd5eb2-965f-4a17-8138-49b52f9dea1b · outbound

This paper cites FedMuon: Accelerating federated learning with matrix orthogonalization.arXiv preprint arXiv:2510.27403, 2025.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FedMuon: Accelerating federated learning with matrix orthogonalization.arXiv preprint arXiv:2510.27403, 2025

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:49.031233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77edb4e7-00b4-40a3-b1d5-7656508e0796 · outbound

This paper cites DP-FedPGN: Finding global flat minima for differentially private federated learning via penalizing gradient norm.arXiv preprint arXiv:2510.27504, 2025.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks DP-FedPGN: Finding global flat minima for differentially private federated learning via penalizing gradient norm.arXiv preprint arXiv:2510.27504, 2025

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:49.036538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d24ccd7-af1b-4783-8165-6bd0bbff9e3c · outbound

This paper cites Federated residual low-rank adaptation of large language models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Federated residual low-rank adaptation of large language models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.151071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ea5c0333-3609-4496-a0ab-456c7dab09cc · outbound

This paper cites Differentially private federated low rank adaptation beyond fixed-matrix.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Differentially private federated low rank adaptation beyond fixed-matrix

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.135444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a51a564f-6bbd-499a-b73b-2ee0b358340b · outbound

This paper cites Towards robust parameter-efficient fine-tuning for federated learning.Advances in Neural Information Processing Systems, 38:141777–141800, 2026.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Towards robust parameter-efficient fine-tuning for federated learning.Advances in Neural Information Processing Systems, 38:141777–141800, 2026

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.119232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 00f38cca-191a-4184-934b-29625f9da03d · outbound

This paper cites Personalized federated fine- tuning for LLMs via data-driven heterogeneous model architectures.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Personalized federated fine- tuning for LLMs via data-driven heterogeneous model architectures

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.103714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f2410eb0-6481-4b62-999d-124bb5c942df · outbound

This paper cites FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:49.061472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4348d85-7a35-498d-8489-5bf5b2cb5934 · outbound

This paper cites FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:49.066405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a97a8a0-9973-4f8c-9632-18a62878574d · outbound

This paper cites Fedsvd: Adaptive orthogonalization for private federated learning with lora.Advances in Neural Information Processing Systems, 38:119733–119757, 2026.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Fedsvd: Adaptive orthogonalization for private federated learning with lora.Advances in Neural Information Processing Systems, 38:119733–119757, 2026

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.087111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b04c7f65-3a38-43f9-aca5-1c39f5848a2f · outbound

This paper cites Towards robust and efficient federated low-rank adaptation with heterogeneous clients.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Towards robust and efficient federated low-rank adaptation with heterogeneous clients

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:49.076811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4497f6ea-d78f-4987-98c9-560dec9ed558 · outbound

This paper cites FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:49.081884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fde271f9-a8d5-424d-9b15-4c6bef37e81f · outbound

This paper cites LoRA-FAIR: Federated LoRA fine-tuning with aggregation and initialization refinement.arXiv preprint arXiv:2411.14961, 2024.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks LoRA-FAIR: Federated LoRA fine-tuning with aggregation and initialization refinement.arXiv preprint arXiv:2411.14961, 2024

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:49.087666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85c710e9-b60e-449b-ab8b-1570631e26e7 · outbound

This paper cites SLoRA: Federated parameter efficient fine-tuning of language models.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks SLoRA: Federated parameter efficient fine-tuning of language models

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.060731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a9c3a082-61f5-4283-908a-603ef7b8e6fc · outbound

This paper cites FedMomentum: Preserving lora training momentum in federated fine-tuning.arXiv preprint arXiv:2603.08014, 2026.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FedMomentum: Preserving lora training momentum in federated fine-tuning.arXiv preprint arXiv:2603.08014, 2026

Reference 85

Resolution: verified exact; raw_fallback, observed 2026-08-14T04:16:49.395973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 09b69a96-c22e-498c-8717-ab4a22911d29 · outbound

This paper cites AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:16:49.274400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 816182de-5424-4f4f-97a4-45b97e30a6ac · outbound

This paper cites Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity

Reference 87

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:16:49.251760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7845cf69-d643-4bb9-86bc-9faa59b845b1 · outbound

This paper cites FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-08-14T04:16:49.227782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 718b39d3-cfac-43e5-bbc9-f62b760cce35 · outbound

This paper cites Florg: Federated fine-tuning with low-rank gram matrices and procrustes alignment.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Florg: Federated fine-tuning with low-rank gram matrices and procrustes alignment

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:50.044986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 251abe8d-81b4-47f2-a720-61dacbd3fc99 · outbound

This paper cites Subspace-Constrained Federated Learning with Low-Rank Adaptation.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Subspace-Constrained Federated Learning with Low-Rank Adaptation

Reference 90

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T04:16:49.202154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e7cecfcb-aeec-4bc3-a889-badf25a96526 · outbound

This paper cites an unresolved cited work.

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks Unresolved cited work

Reference 2025

Resolution: parse uncertain; no resolver link, observed 2026-08-14T04:16:48.758696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers