Paper Citation Record · LEDGER

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach

As of 17 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2505.01075.

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

pith.paper-citation-record.v1
2505.01075 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:33:27.264155Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T01:03:26.037233Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T01:05:16.196768Z

Reference resolution

65 of 65 outbound references displayed

verified exact1
verified fuzzy24
unresolved40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad99a233-ff27-4317-83ff-3351cb9c91e2 · outbound

This paper cites Out of distribution generalization in machine learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Out of distribution generalization in machine learning

Reference 1

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Observation dab9d906-5d86-4a51-b90b-b9101b7452b3 · outbound

This paper cites Invariant Risk Minimization.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Invariant Risk Minimization

Reference 2

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Observation 7cfaf1fb-f6ff-41bd-bd0b-cf3f16d927a9 · outbound

This paper cites SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models

Reference 3

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Observation 2a2aded6-c99d-4890-b07e-7814ffba4620 · outbound

This paper cites Towards federated foundation models: Scalable dataset pipelines for group-structured learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Towards federated foundation models: Scalable dataset pipelines for group-structured learning

Reference 4

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Observation acd831b6-164b-4579-bee1-25eceaaa3f92 · outbound

This paper cites On Bridging Generic and Personalized Federated Learning for Image Classification.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach On Bridging Generic and Personalized Federated Learning for Image Classification

Reference 5

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Observation 1eef3f9d-a88c-4463-97ba-590f7e3d1ca3 · outbound

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

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Heterogeneous lora for federated fine-tuning of on-device foundation models

Reference 6

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Observation 2369c4ba-8ada-4250-8c2f-ce29aa60a718 · outbound

This paper cites Exploiting shared representations for personalized federated learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Exploiting shared representations for personalized federated learning

Reference 7

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Observation c0b614bc-d75d-4dcf-9abc-d5c53e473789 · outbound

This paper cites Dingo: Distributed newton-type method for gradient-norm optimization.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Dingo: Distributed newton-type method for gradient-norm optimization

Reference 8

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Observation 180381ad-e889-4d88-8b60-55944ec103e7 · outbound

This paper cites Distributionally robust federated averaging.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Distributionally robust federated averaging

Reference 9

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Observation d5fc0060-ba4e-46b1-8401-45055bd16afd · outbound

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

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation 975c5b13-fd93-4898-91e7-9ad157e78a44 · outbound

This paper cites The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness

Reference 11

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Observation 27298eee-731a-4030-9e3c-58fdb30ed30b · outbound

This paper cites Fednew: A communication-efficient and privacy-preserving newton-type method for federated learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Fednew: A communication-efficient and privacy-preserving newton-type method for federated learning

Reference 12

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Observation 83c4849b-ef9b-4a9f-bfed-5169d7932bba · outbound

This paper cites Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach

Reference 13

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Observation b45deeb1-95f4-4e75-b63b-551a76e7083a · outbound

This paper cites Active invariant causal prediction: Experiment selection through stability.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Active invariant causal prediction: Experiment selection through stability

Reference 14

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Observation 26034861-aaf3-4756-8aa2-086b08ee9b2a · outbound

This paper cites Out-of-distribution generaliza- tion of federated learning via implicit invariant relationships.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Out-of-distribution generaliza- tion of federated learning via implicit invariant relationships

Reference 15

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Observation 82514353-9111-4dcd-a0de-ab9f2afeb2c7 · outbound

This paper cites Deep residual learning for image recognition.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Deep residual learning for image recognition

Reference 16

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Observation 8ceb8243-5e7e-416e-b4c1-e3df6c8e6113 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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Observation 94fe9d5c-40eb-479a-aa1b-6ed73ff6cca4 · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 18

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Observation e4e575ab-d337-48fd-88f2-254bb68d1814 · outbound

This paper cites Test-time robust personalization for federated learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Test-time robust personalization for federated learning

Reference 19

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Observation b93c17a9-c865-4ccc-a752-518c8d2bf60e · outbound

This paper cites Breaking the centralized barrier for cross-device federated learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Breaking the centralized barrier for cross-device federated learning

Reference 20

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Observation 7fa9dc29-1b47-4315-a27a-13d2f2445ff1 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Scaffold: Stochastic controlled averaging for federated learning

Reference 21

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Observation 225577ce-f9ff-47fb-b5f4-ff5617730548 · outbound

This paper cites Robust learning from untrusted sources.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Robust learning from untrusted sources

Reference 22

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Observation 08cc2fdb-78a5-4424-b6e8-3a4cb7d42cae · outbound

This paper cites When is invariance useful in an Out-of-Distribution Generalization problem ?.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach When is invariance useful in an Out-of-Distribution Generalization problem ?

Reference 23

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Observation db65e06d-2337-4851-9b2a-f8f0f7354c75 · outbound

This paper cites FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

Reference 24

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Observation 643a5bda-b978-42e4-a4b7-80606a400a0e · outbound

This paper cites Ditto: Fair and robust federated learning through personalization.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Ditto: Fair and robust federated learning through personalization

Reference 25

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Observation 33e1aae0-ddeb-4cb2-8c40-56fd7d682297 · outbound

This paper cites Federated optimization in heterogeneous networks.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Federated optimization in heterogeneous networks

Reference 26

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Observation 74c0b3f0-40f6-46bd-8077-192c109d965a · outbound

This paper cites FedBN: Federated Learning on Non-IID Features via Local Batch Normalization.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

Reference 27

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This paper cites Federated Domain Generalization: A Survey.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Federated Domain Generalization: A Survey

Reference 28

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Observation 483cacd0-4fef-4258-b441-1e7791ff2eaf · outbound

This paper cites No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier

Reference 29

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Observation 4f3fbca5-778a-4d8a-9477-34834636fce1 · outbound

This paper cites Heterogeneous risk minimization.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Heterogeneous risk minimization

Reference 30

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Observation 73437bdc-5ecd-471f-b9bd-0d7c00399fd1 · outbound

This paper cites Towards Out-Of-Distribution Generalization: A Survey.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Towards Out-Of-Distribution Generalization: A Survey

Reference 31

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Observation baa8b67a-35bc-49b3-8670-06d0493e958f · outbound

This paper cites Summary of chatgpt-related research and perspective towards the future of large language models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Summary of chatgpt-related research and perspective towards the future of large language models

Reference 32

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This paper cites Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring

Reference 33

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Observation 46bcc355-f31a-49d9-b706-cd5be77d2a28 · outbound

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

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Communication-efficient learning of deep networks from decentralized data

Reference 34

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This paper cites Stochastic gradient methods for distributionally robust optimization with f-divergences.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Stochastic gradient methods for distributionally robust optimization with f-divergences

Reference 35

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Observation 250611b9-0daf-4b7b-b5c3-93a83b8aebf6 · outbound

This paper cites Fedsr: A simple and effective domain generalization method for federated learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Fedsr: A simple and effective domain generalization method for federated learning

Reference 36

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Observation 1c71a02f-61da-4eae-8ecd-391e0e7cfb7a · outbound

This paper cites Regularizing towards causal invariance: Linear models with proxies.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Regularizing towards causal invariance: Linear models with proxies

Reference 37

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Observation 5dfd3693-e0c3-49b9-a1b2-ede9b21848fe · outbound

This paper cites Effect of scale on catastrophic forgetting in neural networks.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Effect of scale on catastrophic forgetting in neural networks

Reference 38

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Observation ba51b9ab-1e63-4413-af4d-a4cb42ef6d9d · outbound

This paper cites Advances and Open Challenges in Federated Foundation Models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Advances and Open Challenges in Federated Foundation Models

Reference 39

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 40

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models

Reference 41

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Improving LoRA in Privacy-preserving Federated Learning

Reference 42

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Personalized federated learning with moreau envelopes

Reference 43

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Towards personalized federated learning

Reference 44

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Observation 9310d89e-0136-4ac3-8450-0b7f5ff2f752 · outbound

This paper cites Is heterogeneity notorious? taming heterogeneity to handle test-time shift in federated learning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Is heterogeneity notorious? taming heterogeneity to handle test-time shift in federated learning

Reference 45

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Observation 3640821a-1c9e-49e7-89a3-c51c71fcd6d6 · outbound

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Learning personalized causally invariant representations for heterogeneous federated clients

Reference 46

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Observation 9caa3182-f72d-438b-a1e7-80b3915e6262 · outbound

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach LLaMA: Open and Efficient Foundation Language Models

Reference 47

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Deep hashing network for unsupervised domain adaptation

Reference 48

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach A Field Guide to Federated Optimization

Reference 49

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Observation 9d856ef9-5b8c-4e30-9217-e79bc6494ffd · outbound

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Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Finetuned Language Models Are Zero-Shot Learners

Reference 50

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This paper cites Perada: Parameter-efficient federated learning personalization with generalization guarantees.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Perada: Parameter-efficient federated learning personalization with generalization guarantees

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:33:28.005362Z

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Observation d3abd4dc-c50b-4812-a350-914fa1b73074 · outbound

This paper cites Fwdllm: Efficient fedllm using forward gradient.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Fwdllm: Efficient fedllm using forward gradient

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:33:27.992279Z

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Observation 1caffebc-aa7d-4070-9507-e7d140eec23b · outbound

This paper cites Federated adversarial domain hallucination for privacy-preserving domain generalization.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Federated adversarial domain hallucination for privacy-preserving domain generalization

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:33:27.979699Z

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Observation f626a73f-b965-437d-99aa-21a9568e4a02 · outbound

This paper cites Causalvae: Disentangled representation learning via neural structural causal models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Causalvae: Disentangled representation learning via neural structural causal models

Reference 54

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Observation fae2a45d-b02e-4686-90b5-258328afebae · outbound

This paper cites Dual-Personalizing Adapter for Federated Foundation Models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Dual-Personalizing Adapter for Federated Foundation Models

Reference 55

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Observation 426013e1-37df-486e-9023-6ee3af81ee83 · outbound

This paper cites pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T04:33:27.052189Z

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Observation 4f9ba258-9968-403e-bac9-d04b783c1a6b · outbound

This paper cites Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T04:33:27.058273Z

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Observation 6d4ea451-fe0a-4552-b8e2-2da432bee1f6 · outbound

This paper cites What Do We Mean by Generalization in Federated Learning?.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach What Do We Mean by Generalization in Federated Learning?

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T04:33:27.063162Z

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Observation 61b50890-3de0-47f7-b7ca-7d760a177e94 · outbound

This paper cites Towards Building the Federated GPT: Federated Instruction Tuning.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Towards Building the Federated GPT: Federated Instruction Tuning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T04:33:27.071235Z

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Observation 06c1985c-e714-49ea-98b1-c72c975ff549 · outbound

This paper cites Federated Learning with Domain Generalization.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Federated Learning with Domain Generalization

Reference 60

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Observation 77a4dfba-466c-454e-b400-4a81d50ef653 · outbound

This paper cites Feder- ated domain generalization with generalization adjustment.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Feder- ated domain generalization with generalization adjustment

Reference 61

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Observation ac27f963-f351-4192-932c-7c7b48e6ee38 · outbound

This paper cites Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre- trained language models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre- trained language models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T04:33:27.173319Z

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Observation 204ef324-1a9c-4d8c-a574-1314b0f54167 · outbound

This paper cites A Survey of Large Language Models.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach A Survey of Large Language Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T04:33:27.249927Z

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Observation 74d3cee4-f6d9-42c1-8a85-358652dd3884 · outbound

This paper cites Fedfa: Federated learning with feature anchors to align features and classifiers for heterogeneous data.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Fedfa: Federated learning with feature anchors to align features and classifiers for heterogeneous data

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:33:27.839106Z

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Observation 1971f6e5-16bc-44fb-81d8-934907bdbae8 · outbound

This paper cites When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions.

Federated Adapter on Foundation Models: An Out-Of-Distribution Approach When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 65

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Pith citing papers

Observation e9afd0c6-6c89-4609-a7a4-8a2ca3f79bf5 · inbound

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Federated Adapter on Foundation Models: An Out-Of-Distribution Approach

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.199014Z

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