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Paper Citation Record · LEDGER

FedMD: Heterogenous Federated Learning via Model Distillation

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 55 inbound Pith citation observations for arXiv:1910.03581.

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

pith.paper-citation-record.v1
1910.03581 v1

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measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 55 of 55 standing notices

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

measured 55 of 55 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:45:42.267385Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

480
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6eef8430-eeeb-45b2-9d24-bbd5fda3085b · inbound

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions cites this paper.

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions FedMD: Heterogenous Federated Learning via Model Distillation

Reference 79

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arxiv_id, observed 2026-05-23T23:48:39.297162Z

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

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Observation 055e9802-4054-4843-82ba-924b91e26cc0 · inbound

GeFL: Model-Agnostic Federated Learning with Generative Models cites this paper.

GeFL: Model-Agnostic Federated Learning with Generative Models FedMD: Heterogenous Federated Learning via Model Distillation

Reference 15

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source=arxiv_source observed=2026-08-11T04:45:42.267385Z digest=sha256:9f5cb058fcf3a92da2bbab09244cea6384f68e20135f3f3ebe71e3db198acf33

Observation 16ca26cd-cb01-4374-8526-5f24a03263cc · 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 FedMD: Heterogenous Federated Learning via Model Distillation

Reference 15

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source=pdf_text observed=2026-08-11T00:10:23.498794Z digest=sha256:4f00be599a5d30045ca4162a1d3df58812150f10b389a679e995f7cc2b95f808

Observation 6c63ba58-4a1d-442f-9e83-2da6d765a755 · inbound

A Robust Federated Learning Framework for Undependable Devices at Scale cites this paper.

A Robust Federated Learning Framework for Undependable Devices at Scale FedMD: Heterogenous Federated Learning via Model Distillation

Reference 68

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source=pdf_text observed=2026-08-10T23:45:18.391443Z digest=sha256:1d09c840a18fd95626608dffd4a93382a375968dda416bb48d357e525804cdd5

Observation b767fe50-9596-4f09-b29b-d0f4139476bf · inbound

Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection cites this paper.

Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection FedMD: Heterogenous Federated Learning via Model Distillation

Reference 18

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source=pdf_text observed=2026-08-10T23:02:02.965242Z digest=sha256:29f43493d2dcdf8571a8e4cdb970e2c1e17d8f477b921b6b5d0cb608fea571cd

Observation f29dcaaf-2f7e-41d0-90c5-d2b02a9ec76b · inbound

A Survey of Secure Semantic Communications cites this paper.

A Survey of Secure Semantic Communications FedMD: Heterogenous Federated Learning via Model Distillation

Reference 56

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source=pdf_text observed=2026-08-10T22:44:11.275849Z digest=sha256:3ab004e67b9c0010c60951b618dff6e7c112869890196dee1b3bff9eafc90ec9

Observation 0a37434d-c1a8-4b95-9de3-5fe96e3b9000 · inbound

Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection cites this paper.

Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection FedMD: Heterogenous Federated Learning via Model Distillation

Reference 19

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arxiv_id, observed 2026-05-23T05:42:36.313437Z

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

source=pdf_text observed=2026-05-23T05:40:43.227597Z digest=sha256:e2074892a42c31880f69b14fec6765c16383f2d73ed57d0da899b0dcdca8fe2b

Observation ac89da33-173a-407a-878f-5b54b9ea53e3 · inbound

Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models cites this paper.

Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models FedMD: Heterogenous Federated Learning via Model Distillation

Reference 31

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source=arxiv_source observed=2026-08-10T15:35:30.465042Z digest=sha256:7a2a2b3cf69567a4d3c3d99d538c32c5158a4e9f3d71059b9e19f0d806cc8806

Observation b165ebc1-371a-4523-9283-15194ea3764a · inbound

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning cites this paper.

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 18

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source=pdf_text observed=2026-08-09T18:53:30.802782Z digest=sha256:32e0db46eced59155fbe4a7cccf4c568800019682c12862587ca2c523ec7b292

Observation 1ab93efb-5daf-4d5e-8994-8bdbe05014a5 · inbound

FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation cites this paper.

FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation FedMD: Heterogenous Federated Learning via Model Distillation

Reference 23

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source=pdf_text observed=2026-08-09T17:31:58.289813Z digest=sha256:faea595770f91be2692b4c0a8fe438c67a1959bf40f2c84ce0590a549f2adcb4

Observation 23cbcc7f-b55e-42e3-82c2-6a2cb75e9409 · inbound

Interaction-Aware Gaussian Weighting for Clustered Federated Learning cites this paper.

Interaction-Aware Gaussian Weighting for Clustered Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 1998

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source=pdf_text observed=2026-08-09T05:10:22.233362Z digest=sha256:6e04d5898d5b57c05ef57aac0e34f878c5c5437a4dd54743d8583aa5171c079c

Observation 3a25adac-4864-4777-b8cb-5ebe32aacbf3 · inbound

Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation cites this paper.

Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation FedMD: Heterogenous Federated Learning via Model Distillation

Reference 22

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source=pdf_text observed=2026-08-08T11:15:28.494286Z digest=sha256:9405ce14adc7b11ff9e2182cca2b8c140fc8330c61d90a19e611b9f69fcaf66c

Observation 2f21efba-c8f8-43c9-ba44-1cac25d8448a · inbound

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices cites this paper.

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices FedMD: Heterogenous Federated Learning via Model Distillation

Reference 38

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source=pdf_text observed=2026-08-08T04:53:59.027350Z digest=sha256:605d82586b2f30d9616707cda431397458b49411210b3616fb86be58d3ae492b

Observation b7d12fd9-c9ff-43f3-83a1-c3e69fa548dc · inbound

Federated Learning-Distillation Alternation for Resource-Constrained IoT cites this paper.

Federated Learning-Distillation Alternation for Resource-Constrained IoT FedMD: Heterogenous Federated Learning via Model Distillation

Reference 25

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source=pdf_text observed=2026-08-07T14:00:34.658378Z digest=sha256:56da6771df7f471aef8dccc3ce33eedc5afa61a5906f68ee1d8e7f6a6145030c

Observation c437739f-4c3e-4780-b3e2-6ada7878efc0 · inbound

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data cites this paper.

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data FedMD: Heterogenous Federated Learning via Model Distillation

Reference 24

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source=pdf_text observed=2026-08-07T13:59:35.585281Z digest=sha256:a197290bf4c8d98d5d242479ffe84f0a94aa7dcefede65d27271ca8f62076e05

Observation 463ffae0-8d4a-4741-8688-57a4af3a6b49 · inbound

Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms cites this paper.

Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms FedMD: Heterogenous Federated Learning via Model Distillation

Reference 79

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source=pdf_text observed=2026-08-07T13:28:19.799625Z digest=sha256:5ed39335c420b26ab2f8460f5e68f717986b1d51cc64227ba39925a11de7e823

Observation 0c833a7a-7c81-4444-95f5-b1f4b7f12ec0 · inbound

SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks cites this paper.

SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks FedMD: Heterogenous Federated Learning via Model Distillation

Reference 21

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source=pdf_text observed=2026-08-07T13:58:00.936539Z digest=sha256:f572c4614d75a68d3ac4ba1f1c80a6bb5f16e1a554e7e3c6929eb3a51d7d9f99

Observation eb2c9333-638a-4140-99a3-b8b3423266ae · inbound

Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions cites this paper.

Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions FedMD: Heterogenous Federated Learning via Model Distillation

Reference 1998

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source=pdf_text observed=2026-08-07T11:19:03.359170Z digest=sha256:603b535757e8cbfddf58d846d838f28ac57397f222b87149b21516d9e0342386

Observation f36b29f5-c643-46ef-abc7-ca222c954270 · 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 FedMD: Heterogenous Federated Learning via Model Distillation

Reference 30

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Observation 2e010ee3-a1d7-4bb3-b496-955547203c8e · inbound

FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data cites this paper.

FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data FedMD: Heterogenous Federated Learning via Model Distillation

Reference 25

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source=pdf_text observed=2026-08-06T22:59:37.219227Z digest=sha256:aa89f389c6bf4b487d86b4ddf3f99330e944d93189b08408771faf102fae1604

Observation e8a084e9-06b3-4303-9b2a-9a1b910f8c3f · inbound

Heterogeneous Federated Learning with Prototype Alignment and Upscaling cites this paper.

Heterogeneous Federated Learning with Prototype Alignment and Upscaling FedMD: Heterogenous Federated Learning via Model Distillation

Reference 15

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source=pdf_text observed=2026-08-06T19:55:03.419039Z digest=sha256:eba062094bf005a2a7128572debf647d4b495b0a91179a3375e1d19f63cd00c0

Observation 9ebd229b-ba65-45e6-a435-78762dbdfb95 · inbound

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments cites this paper.

TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments FedMD: Heterogenous Federated Learning via Model Distillation

Reference 12

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source=arxiv_source observed=2026-08-06T19:58:03.877147Z digest=sha256:4335b20bb91cfc4373091524b0f48074356987077bb2fe23f220ea3716a8d484

Observation c5291661-0de6-42a8-b847-6bee5605ef11 · inbound

Hypernetworks for Model-Heterogeneous Personalized Federated Learning cites this paper.

Hypernetworks for Model-Heterogeneous Personalized Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 18

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source=pdf_text observed=2026-08-06T11:55:31.545915Z digest=sha256:03f12408a8778e63070b4cdce0028241827088958bcb4a57c6e645dc568a5bd4

Observation 8a212164-63e4-45b9-a13b-1d148c7c1299 · inbound

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer cites this paper.

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer FedMD: Heterogenous Federated Learning via Model Distillation

Reference 21

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source=pdf_text observed=2026-08-06T05:33:39.913013Z digest=sha256:d15b082ce9b2668d2885bce26f51fbcfb15aca6a995d1ed5cd567efebe79aa3c

Observation e2c59113-cdad-4c6c-82a5-2ad5627f775d · inbound

Heterogeneity-Oblivious Robust Federated Learning cites this paper.

Heterogeneity-Oblivious Robust Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 18

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source=pdf_text observed=2026-08-06T04:27:20.046428Z digest=sha256:8632212cab947153ddc1bc7224158e51887576b6ae06fde3f32d60a5ab99580a

Observation 8b14e4cf-5666-4081-8552-73745ff23761 · 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 FedMD: Heterogenous Federated Learning via Model Distillation

Reference 26

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source=pdf_text observed=2026-08-05T20:33:21.591889Z digest=sha256:fb61f4ddb22f87920501ef00d69fa667bd86db196659800aa1dbf57f3fad7d94

Observation 3c3b7dfe-71b1-4bfa-9bc0-ede69c42e1f1 · inbound

Generalizable Federated Learning using Client Adaptive Focal Modulation cites this paper.

Generalizable Federated Learning using Client Adaptive Focal Modulation FedMD: Heterogenous Federated Learning via Model Distillation

Reference 34

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source=arxiv_source observed=2026-08-05T20:19:06.321027Z digest=sha256:f66d4b41257be54d76fedf4cb2ea18ae4243b6ab128168ca47021d18aa5167bd

Observation 0b63a6ff-67ea-442f-9709-cdf69aed56eb · inbound

Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks cites this paper.

Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks FedMD: Heterogenous Federated Learning via Model Distillation

Reference 6

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source=pdf_text observed=2026-08-05T12:17:48.787028Z digest=sha256:ddc5fdee3db0dab8b7438e46e57485534e7ce1736a8657f084ecf466e995ca78

Observation 061b6739-b7d5-4e3e-8a3f-0642c3cccd0a · inbound

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization FedMD: Heterogenous Federated Learning via Model Distillation

Reference 128

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source=arxiv_source observed=2026-08-04T21:06:26.291845Z digest=sha256:3b661fee55c34df08ffb97a5c2b523a84479579eae8a8a2abbad65af7548a189

Observation 4d06c913-0d1c-4f77-9384-64b44fa79d5f · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion FedMD: Heterogenous Federated Learning via Model Distillation

Reference 80

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arxiv_id, observed 2026-05-11T12:56:06.324393Z

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source=arxiv_source observed=2026-05-10T02:35:40.593397Z digest=sha256:0a156fd4f165f8e4312d4fa489ab78523d5de85320103b789ee99af6cb3ed9ad

Observation 9cb440be-5b8c-4518-a633-e11c61adf73f · inbound

When To Adapt? Adapting the Model or Data in Federated Medical Imaging cites this paper.

When To Adapt? Adapting the Model or Data in Federated Medical Imaging FedMD: Heterogenous Federated Learning via Model Distillation

Reference 16

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arxiv_id, observed 2026-05-11T15:11:06.039006Z

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

source=pdf_text observed=2026-05-09T20:32:49.268444Z digest=sha256:042b7dfd704f8ffa0357c60c0ac3ee8e1cc1892bca7ccced217c64c7bb35ac91

Observation afd47aca-a379-40af-bbd1-84908542c82d · inbound

Learned Neighbor Trust for Collaborative Deployment in Model-Agnostic Decentralized Learning cites this paper.

Learned Neighbor Trust for Collaborative Deployment in Model-Agnostic Decentralized Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 16

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arxiv_id, observed 2026-05-11T18:01:06.511126Z

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

source=pdf_text observed=2026-05-08T16:46:23.457420Z digest=sha256:10ecaaee8da4303a4df591edb371d6d01c5958a618a020ae6509eab72da7f849

Observation 1cddeaa6-b3fc-435e-94c1-cd0e412c99f1 · inbound

From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning cites this paper.

From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 13

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arxiv_id, observed 2026-05-11T19:51:10.792471Z

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

source=pdf_text observed=2026-05-08T10:54:37.847575Z digest=sha256:094444cdf5cc5b93b530f14eeb391d4bcf5638098765334debaeabbf3b5f9aef

Observation 92a3f5fe-75a9-4538-a91b-cbaca25bbdb6 · inbound

HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning cites this paper.

HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 14

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arxiv_id, observed 2026-05-11T02:45:58.341487Z

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

source=pdf_text observed=2026-05-11T02:42:40.417350Z digest=sha256:e60fb69cfd662104088952d8fb6ba82fc38667b37905e4eb3488570d1987ef46

Observation 44dc985e-03ae-4c43-b3c9-c6752abfa761 · inbound

Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective cites this paper.

Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective FedMD: Heterogenous Federated Learning via Model Distillation

Reference 29

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arxiv_id, observed 2026-05-12T06:36:26.917954Z

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

source=pdf_text observed=2026-05-12T04:07:54.716306Z digest=sha256:d3e3000ef31a86acff7870eb354f47426683ccb7ded950fcff13d832bbdc2177

Observation 99c2508c-2361-4cc8-a2f7-11c72a4ed7a3 · inbound

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication cites this paper.

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication FedMD: Heterogenous Federated Learning via Model Distillation

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T06:07:22.298010Z

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=2026-05-13T06:05:02.954856Z digest=sha256:afa4f738620e7ca1ac9e06648ac0e650d8c647a4e644ca2fd90440d75744d7b1

Observation 3e2a5bc9-3624-46ce-8455-88922f09b987 · inbound

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication cites this paper.

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication FedMD: Heterogenous Federated Learning via Model Distillation

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T14:05:46.263610Z

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=2026-06-30T22:21:47.617478Z digest=sha256:d8360335101d521be8d152e2ad1bad5bcf8eea5fc171a69966e1e16e0b2b333a

Observation 6612cef7-9c9a-42eb-b06c-93864695a54e · inbound

On What We Can Learn from Low-Resolution Data cites this paper.

On What We Can Learn from Low-Resolution Data FedMD: Heterogenous Federated Learning via Model Distillation

Reference 31

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T05:32:19.053106Z

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=2026-05-13T05:28:50.993737Z digest=sha256:f350a2df65f84ca596d59c7c1b6b55774f286bd58628cfc9d37010665dbe9b44

Observation ad3dc307-8aa2-4e43-9c96-93193e34c76a · inbound

BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring cites this paper.

BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring FedMD: Heterogenous Federated Learning via Model Distillation

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-15T03:14:52.978553Z

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=2026-05-15T03:12:52.266671Z digest=sha256:62cead889cc10f44f81491e6d51245a96e49ea8efc4bb8737087e4c7135c81a9

Observation 71cd7a9e-cf93-4a85-99c3-89a4d7c045a5 · inbound

Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning cites this paper.

Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-20T14:13:21.227560Z

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=2026-05-20T14:11:53.371521Z digest=sha256:a70d8a17c45b0460e03f3449930250042a387f1cd818f8694b5bf934caf69c46

Observation b38888b5-99e4-4617-a529-d90052e3842a · inbound

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning cites this paper.

FedQHD: Closed-Form Function-Space Federated Reinforcement Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T13:33:28.493482Z

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=2026-06-29T13:24:31.382577Z digest=sha256:1d9cfeee103c1cb4ee89ed585f534e0334461ef192d896da785ec0c14ce62518

Observation 71ab4c0b-e763-4d6a-8d2a-23019047f915 · inbound

FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment cites this paper.

FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment FedMD: Heterogenous Federated Learning via Model Distillation

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:06:15.807959Z

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=2026-06-28T15:51:08.954790Z digest=sha256:577d928f8fb100ceac5c621ea71cebddce329a452ba6267dea9491fa7eb0e355

Observation 50f42fd3-230c-4a32-9515-393dbffc8b7a · inbound

Efficient Federated Estimation and Inference for High-Dimensional Tail Index Regression cites this paper.

Efficient Federated Estimation and Inference for High-Dimensional Tail Index Regression FedMD: Heterogenous Federated Learning via Model Distillation

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T04:26:35.440933Z

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=2026-06-28T09:13:24.955608Z digest=sha256:57c7b5ff168ce2a3d11616e8ffc265873dc2a22b9f631eabe9e4442340de08c4

Observation 77276518-85ba-4c02-b758-bbfff9f55270 · inbound

HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning cites this paper.

HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-28T19:32:34.796418Z

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=2026-06-28T19:30:47.092498Z digest=sha256:108a1e0f17fac50c8da738f3e8939e1af94662e1228634a1566af7722a816570

Observation 9b5376a6-5ea8-480b-b820-cab435779b37 · inbound

Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning cites this paper.

Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:07:25.935692Z

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=2026-06-27T19:17:23.711817Z digest=sha256:7177c054814f08f844163b1755e55eacaeadc45317f98dd0e51c31d8c1511caa

Observation 2966c8dc-181f-48a2-8dfd-e3e0d5228df4 · inbound

Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning cites this paper.

Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-03T01:07:29.737905Z

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=2026-06-27T16:51:20.071589Z digest=sha256:044067b0f3b46bea8f13878d103eb3289f4d0127eb6d90f81e7a936322b9436d

Observation 55cb39bc-79af-40a4-a6c2-1f36cf9b7a74 · inbound

Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation cites this paper.

Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation FedMD: Heterogenous Federated Learning via Model Distillation

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-03T04:17:37.618898Z

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=2026-06-27T13:59:25.075112Z digest=sha256:2fcdfa654d96701b135a92061803bb8f368d26f7d7fb0c63494cf9770afbe598

Observation 9ec1d695-e28d-458d-bccd-7358d602fb49 · inbound

Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage cites this paper.

Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage FedMD: Heterogenous Federated Learning via Model Distillation

Reference 125

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T16:44:56.438631Z

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=2026-06-30T04:41:41.370083Z digest=sha256:e3c9fbc4494573c3f1c459d5029b2f418933e6cd2eb8679f8d5daf7ed72ba7dd

Observation 35ba51d8-61e9-4018-8d08-18c5dd1f19c2 · inbound

TallyTrain: Communication-Efficient Federated Distillation cites this paper.

TallyTrain: Communication-Efficient Federated Distillation FedMD: Heterogenous Federated Learning via Model Distillation

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T19:47:18.789510Z

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=2026-07-02T19:44:47.733008Z digest=sha256:9f0745241b83a87780e3f650ef9f1617dea5f2de0751b17e44a249b0338e326d

Observation 111293dd-bedd-4e71-96de-4b48d9469316 · inbound

Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems cites this paper.

Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems FedMD: Heterogenous Federated Learning via Model Distillation

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-07-10T13:47:05.782452Z

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=2026-07-10T13:44:13.951954Z digest=sha256:6987a9c9c87b9ca12bcc42c6922ee56dd0144a1dee1af1dc21a58e67f6dbf37b

Observation 433e6942-168b-4133-9aa1-3e56899999b5 · inbound

Federated Lightweight Fine-Tuning cites this paper.

Federated Lightweight Fine-Tuning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-01T17:36:04.614851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T17:36:04.614851Z digest=sha256:b5d3abfc79a3f5cfc698053d8f5b3b7ae26018565ecf0f73f2f46c49e3e85978

Observation 6adb613a-878a-4f93-9bd0-8b9e394f0dd0 · inbound

Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference cites this paper.

Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference FedMD: Heterogenous Federated Learning via Model Distillation

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-01T10:45:24.494754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T10:45:24.494754Z digest=sha256:28ebed33259ffbee8221dd48c726ba8993b820e78bb95ab21e8a2119bad2cfd4

Observation 479b3c50-4af9-462a-91b9-35a430cea43d · inbound

FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning cites this paper.

FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-30T20:41:06.900821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T20:41:06.900821Z digest=sha256:7f9266ad57fefce728d78b80ab9741745b24a1cde88483ed95e9831977645ba1

Observation 1c5d94b1-a5e4-4c83-9aa8-f6e991fd898f · inbound

FedJigsaw: Multi-Agent Collaborative Model Reassembly for Decentralized Heterogeneous Federated Learning cites this paper.

FedJigsaw: Multi-Agent Collaborative Model Reassembly for Decentralized Heterogeneous Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-04T19:27:40.407990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T19:27:40.407990Z digest=sha256:9cabe1537f63b53c0cb6a8066263527e9a010dcca23e013a4ca76b15c237cdd7

Observation 599ff869-d86a-4c33-970a-27e80bab24d2 · inbound

AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning cites this paper.

AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning FedMD: Heterogenous Federated Learning via Model Distillation

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T21:03:54.915167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T21:03:54.915167Z digest=sha256:3bf9d99656cecdab9fc4fe874aafdd8472e1876220f10882682d72169a8c4f6f