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Paper Citation Record · LEDGER

Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2402.11505.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2402.11505 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:26:47.973305Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T13:16:58.669038Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 517a7ba1-f2a3-4908-b66b-aaa996803aa3 · inbound

Decentralized Low-Rank Fine-Tuning of Large Language Models cites this paper.

Decentralized Low-Rank Fine-Tuning of Large Language Models Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-10T14:26:47.973305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:26:47.973305Z digest=sha256:f67c9e7265912646a83302d7598f870e35e1454d690332ccc717091a25911311

Observation c5545f86-67e6-4bbb-ba1b-9e7e21e5b4a7 · inbound

Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs cites this paper.

Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-09T20:20:45.673101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T20:20:45.673101Z digest=sha256:81abb864d05952fdb133791ef7bfde7b85cde0d5e5623eb62781e8ae53984c02

Observation 6f57010f-4df7-434a-bbc3-2893d74b5313 · inbound

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA cites this paper.

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T11:57:24.925571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:57:24.925571Z digest=sha256:fda61d747f5a5ef7da973ea37d4ab41c249c374a6d87e25f52d9a63541350fc9

Observation ae1ff209-c84f-459b-b12b-8d7d97bcbfec · inbound

FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model cites this paper.

FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-22T01:44:30.393366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T01:43:44.406488Z digest=sha256:e667918a7f6aa9745ed513133c448c08e2e99fa9a11b67ecfca49a100c0fdf62

Observation 37add713-bd64-480a-ac98-ad05fa1a5df1 · inbound

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design cites this paper.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T14:49:56.588435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:49:56.588435Z digest=sha256:579e69c3962954f3ef308ddf46042bb41a4041c610a5c61fa2ba55e04c89ceea

Observation bd136f6e-2fc7-418f-8590-f6fd666fe6bd · inbound

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data cites this paper.

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-05T05:38:49.539833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:38:49.539833Z digest=sha256:2e4a33d6617206bd3e380053d6656af3e771e8ffd6442b683285a799c4918665

Observation d4bbea2e-6bd4-497b-8ca5-f0bd630085e9 · inbound

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models cites this paper.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T19:08:54.369245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-20T19:06:08.951475Z digest=sha256:95b57857711cb8226afbc6129230ac35004c073efe37ac4295cb62900bb433c9

Observation 18271e7b-d035-4402-8168-a5e94dde559d · inbound

FedSDR: Federated Self-Distillation with Rectification cites this paper.

FedSDR: Federated Self-Distillation with Rectification Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T12:18:16.229864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-20T12:18:10.362573Z digest=sha256:65ff69facf7c2e0ee1969c7e01387d94b8c9219cfdffd8312fc9a3c42fdc5d0d

Observation e730c1e1-d646-45e4-b77d-54135d1902c0 · inbound

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models cites this paper.

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T13:16:58.670594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T01:27:04.241484Z digest=sha256:d21071996adbd41be35ed91689008b996bcd335dc469172647dfcc179238dbff