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Paper Citation Record · LEDGER

FedMD: Heterogenous Federated Learning via Model Distillation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 49 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

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 49 of 49 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 49 of 49 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:53:30.802782Z

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T23:47:28.874336Z digest=sha256:357ce932ac1a77555c5fed6fbed19538912b1699e6a40935d823815e4704b878

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T05:40:43.227597Z digest=sha256:447be2174652833e2131ccfa9ccac19c3a725c8e4740620c84134b4d832236b7

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=sha256:2281d7865679fba1727a1d14fee15cf240365cb80e846be1ccb37187ec3e29b7

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=sha256:4b29845dd2e3756d9fe0fd437fd6fe5bfba09d753c9674cb2f3f84ce0c50cc27

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=sha256:28926b53c11009a4de4d4a69ed75001d794f0a728482f4c785d63418c4b0d63b

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=sha256:7f7017b7ae89c36bcefebbc15523c9deccbd30deb29b2e12cd7af8cc3b582ea6

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=sha256:2a9c258f3d25577e1904233bdef6240dde9ca80e797d5f8090649796dd6d6366

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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source=pdf_text observed=2026-08-07T01:03:39.763732Z digest=sha256:f5a486f8fd8d60bcc354b9c806a72e2cf9813a6c669e82959a5a594647ed6acb

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=sha256:47b20dd76078f0e53c8a0665e379bfd2fba4bdf32c56af783eb9b5309ab5a94b

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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source=pdf_text observed=2026-05-09T20:32:49.268444Z digest=sha256:cb00f948c5b6131e78395aeeddc32399c99228c8579183df0748c10286f9e766

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T16:46:23.457420Z digest=sha256:ce3f9e162dabc745ec8aefcce1fea6988cc1dbd54dba08c591844119ae840a23

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T10:54:37.847575Z digest=sha256:48bfa2aff422519c4bfafd83c3c2cae45b6c8a0430eaf0d501f2bb31da63e80f

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-11T02:42:40.417350Z digest=sha256:5274b1a1056dca015e2316201d76eaf78e4cffd8eac8f5d4f8e1b5113728e6e2

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T04:07:54.716306Z digest=sha256:0f93b92b0580d6cf31fbae5ac7f360c1babc8b4287b83952d14712b98e9e8915

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

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

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

source=pdf_text observed=2026-05-13T06:05:02.954856Z digest=sha256:bccf5d5b507d211bc52b069dd7e74a4622216ac726e22774061141203b30adf9

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

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

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

source=pdf_text observed=2026-06-30T22:21:47.617478Z digest=sha256:d9e9d6bf9f8ff5a66cdae731564d6790695a5b5c9454003b1a63c75d7e42dfcd

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

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

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

source=arxiv_source observed=2026-05-13T05:28:50.993737Z digest=sha256:6c9d3694ccc258104040b9ef9015cc8045dd41d4722d7d2763c73e48bcee76c9

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

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

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

source=pdf_text observed=2026-05-15T03:12:52.266671Z digest=sha256:6461205d65cb3583530739e3e116857df291549319339682c49be6b115a7d7c0

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

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

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

source=pdf_text observed=2026-05-20T14:11:53.371521Z digest=sha256:cd1a8b2173a91541557fc79d593af43c1035f38d80b2b2b87f0c39e37330440e

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T13:24:31.382577Z digest=sha256:59c0d68adcf3759701ad861ec9a09382d19cf44b648b8047fa5e49e5b0ef97bc

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-28T15:51:08.954790Z digest=sha256:300daa8a41aaba6e09a1aca6ed8ed5b3e1d3d2b8a2e4bf21fa7c5fa01521c352

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T09:13:24.955608Z digest=sha256:e22358fa61e1b6b5224620b70b5179d37ca5b50b75bd011c9ce761fc4f0efb5a

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-28T19:30:47.092498Z digest=sha256:8be929c311d6ea2e4d57374bc18525137f7b198986f28f12ff29d70bdf592e09

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T19:17:23.711817Z digest=sha256:f7d817852c52f8c2cd62f8b03767e8d0978a08d75a69af5bf34e9ca66e09f837

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T16:51:20.071589Z digest=sha256:b04c15e568fc9ecba9a5dabac5a03b82bf5528f68f7ff0fcf709f6b2e5e17b7b

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T13:59:25.075112Z digest=sha256:2c10c01bf4da1c2242f8b1b3c8a0fed58d4b876ff48c87abdc8e2f6301a21075

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-30T04:41:41.370083Z digest=sha256:7dab879e22d6171b25de9e10f8766180191147afa77da53a8d98a1cf221dd3c0

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-02T19:44:47.733008Z digest=sha256:9f897260d50daabc8d23351d339f5d5a0d734b51fb58e1c4da08c3bea2296926

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-10T13:44:13.951954Z digest=sha256:9e574dd8d79d01e34a892e860d172e949ea86ae99a142c472e0cc10ffec2e51c

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:bf071547bffeef28c424146bafc736abdf9d4654eacbe71216482bd4854bed4c