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Paper Citation Record · LEDGER

When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2306.15546.

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

pith.paper-citation-record.v1
2306.15546 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:59:18.803180Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T15:34:57.812220Z

Reference resolution

0 of 0 outbound references displayed

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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 6f994a7b-51ad-4c44-9e84-7fd83de22f57 · inbound

Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach cites this paper.

Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 47

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unresolved
no resolver link, observed 2026-08-11T05:59:18.803180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:59:18.803180Z digest=sha256:d8a8a9abb31f7df8dcab44107a3c480e77b7e8c7ad3dbeb1081ded5b1bb0b18a

Observation 3ef68076-d8cc-4df9-a923-54b036a669a0 · inbound

Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis cites this paper.

Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 54

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no resolver link, observed 2026-08-11T00:10:23.885995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:10:23.885995Z digest=sha256:4caf1cad1178a5e4cc7a2f56b355e0f77e9cc156e84fbb7f0c544a1d8e74149b

Observation 5832320a-03b3-4b15-90ab-800b69b50a2c · inbound

Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning cites this paper.

Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 45

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no resolver link, observed 2026-08-10T22:29:19.838693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:29:19.838693Z digest=sha256:8907bf760fedaaada3eb3b395b2d8ddd4e1520a875c3ff42f9e5fbe1526398f6

Observation 9019eee1-f4a3-47cb-a50a-fcbfc58fd0ac · inbound

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions cites this paper.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 5

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no resolver link, observed 2026-08-10T21:36:09.168255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:36:09.168255Z digest=sha256:c291ce3055195a87830f46069fa002e9e08f73ad3f81c56fcda8dc4ad2185fbd

Observation 172c19ca-c4dc-4602-b3e9-fb15f0c73473 · inbound

A Survey on Foundation Models for Personalized Federated Intelligence cites this paper.

A Survey on Foundation Models for Personalized Federated Intelligence When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-22T15:34:57.814391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-22T15:32:15.293888Z digest=sha256:8aced44eaad8c9d36c909688f5a1a0f62a97d17d5374158c42aed32028e248e0

Observation fc76bdf5-91ef-4412-93ed-c57e1b8aedf4 · inbound

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

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 1

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no resolver link, observed 2026-08-07T14:36:17.993928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:36:17.993928Z digest=sha256:94f6e2c8d56122762525831f5848fa9431e84b1352658d7b3949c2e36db36949

Observation e2c01c79-c464-411d-9092-cef4b78746c8 · inbound

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection cites this paper.

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 41

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no resolver link, observed 2026-08-07T12:05:54.828295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:05:54.828295Z digest=sha256:108581476d3a5b4fb4d51e23a6b66b135490328809101fb887bbfca250db5851

Observation 33945407-5d97-4662-8645-ccb654ace330 · inbound

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR cites this paper.

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 34

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no resolver link, observed 2026-08-07T10:19:48.331580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:19:48.331580Z digest=sha256:8409ce1e290eccf90a14289e891dbb27948ca8ea4aca24ac95877b7140fff6e6

Observation c7c0a385-89d6-4c65-abe0-60a4fc34a601 · inbound

A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation cites this paper.

A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 79

Resolution
verified exact
arxiv_id, observed 2026-05-19T09:32:16.557022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-19T09:28:32.185398Z digest=sha256:cdb7dfd74a8438f9f16832b6b73e65419568b2c248173588cefb6ef1e945ab4d

Observation 73dc3062-6bc7-4978-8d56-d9eafedb44fa · inbound

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation cites this paper.

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 72

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no resolver link, observed 2026-08-07T01:03:43.510839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:03:43.510839Z digest=sha256:1d50853dad6a81ea329e106b6a81519c6c82a8c22404b8065fb849553039dca5

Observation f2d94276-d50c-42b7-ae6e-c4e64d4208b9 · inbound

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models cites this paper.

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 24

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no resolver link, observed 2026-08-06T23:19:34.898298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:19:34.898298Z digest=sha256:93ebaf0546038b99f6cd131ba05815d4c733a50d4894e678d37300a6907dd77c

Observation a8969388-64eb-45d1-8970-372af9273cd9 · inbound

FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models cites this paper.

FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 7

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no resolver link, observed 2026-08-06T19:11:22.712277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:11:22.712277Z digest=sha256:3188b3e76a9240a18b810f1089b0a19b7b9950624a26ef2bbb77ad7cb2797b0b

Observation 2b71659f-a181-45cc-a244-55cfe7fcd25c · inbound

Scaling Decentralized Learning with FLock cites this paper.

Scaling Decentralized Learning with FLock When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 29

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no resolver link, observed 2026-08-06T15:40:19.442782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:40:19.442782Z digest=sha256:c425f5b77dc0e93f3507a6a88decbbf3b0fd5e3765646605d3c22dd61e5cc61f

Observation 2d1eb8ec-e472-480c-a81f-4e09d1b8ee0a · inbound

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models cites this paper.

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 12

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unresolved
no resolver link, observed 2026-08-05T20:33:20.448378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:33:20.448378Z digest=sha256:072c39816924a78963aaba203cddc0c171868ee24eacd87c147f67c3ae3706a4

Observation fe2a3f46-fee7-4188-9920-2bbbe1a0923b · inbound

BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Web cites this paper.

BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Web When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 116

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no resolver link, observed 2026-08-05T18:55:33.803081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T18:55:33.803081Z digest=sha256:51664bb537d98471e3bed0351b726e13951dd919a2ef024a2d36698783d0bd5c

Observation 62669d8c-1e79-41d5-b5ee-343ad64e9cf0 · inbound

FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free cites this paper.

FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 51

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no resolver link, observed 2026-08-05T12:23:54.359683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T12:23:54.359683Z digest=sha256:6eba728b3871716c774aa6ad58d35e5ff4b1d99cde8bd85d60aba648148a48c2

Observation fd755cb6-de30-443e-9d19-c2f544c15639 · inbound

Task-Centric Personalized Federated Fine-Tuning of Language Models cites this paper.

Task-Centric Personalized Federated Fine-Tuning of Language Models When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:27:59.627493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-14T21:26:17.264779Z digest=sha256:c526ac1bfdffbb02ee7ea2879e48426e1fc612c679c4fc2c63becd8644c353fe

Observation 4a410fe2-a495-4ed6-b6e5-ffb54e75af45 · inbound

FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training cites this paper.

FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 46

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T17:31:08.752069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-08T17:28:28.308039Z digest=sha256:bd5be4fd26a8955f6dbc48bfd915ad0d6cb89034e08f90cb48c0b9847ac65552

Observation 744239e0-fd3e-4bbb-9dc5-de02421523fa · inbound

FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training cites this paper.

FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 31

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metadata mismatch
arxiv_id, observed 2026-05-12T06:11:25.490040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-12T04:30:31.877453Z digest=sha256:bc5569bea5a33db4c03821843f868d089953d9421e7fa09e4f68f368730768e6

Observation 5bbeda74-911f-4c9e-9083-edffce5f87dc · inbound

FM$^2$: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging cites this paper.

FM$^2$: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 59

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no resolver link, observed 2026-08-02T05:28:45.032058Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T05:28:45.032058Z digest=sha256:7d76fb08a712d3b6497bbd1e39c43e24f7360e7ff7b57566fcb9ecca1970a692