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Paper Citation Record · LEDGER

Federated Mutual Learning

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2006.16765.

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

pith.paper-citation-record.v1
2006.16765 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:12:57.883322Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T23:52:17.065932Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8a7633fe-b8dc-4c68-aaa8-bcf96e895d7e · inbound

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

GeFL: Model-Agnostic Federated Learning with Generative Models Federated Mutual Learning

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-11T04:45:42.307071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T04:45:42.307071Z digest=sha256:e6e35e73cc542fdfa11b3f86342f59b5c48220f06a189dd05c2e991aa64ccac8

Observation 7a0a47b7-173f-49c2-af8a-a9ba732d76db · inbound

PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning cites this paper.

PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning Federated Mutual Learning

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-09T19:22:38.046234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T19:22:38.046234Z digest=sha256:c845348ec3a945dd916d40a7d0882e3c490b35128828ff3f9152800f9f9c0d7c

Observation 63928846-d83e-4416-a926-aec72f698096 · inbound

Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning cites this paper.

Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning Federated Mutual Learning

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-22T23:52:17.069176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-22T23:49:05.925589Z digest=sha256:0bd6d58dc8cef6f8e5ee8c9d92ea9c46f0715cc4ba0a44839a7119849e6c0cfd

Observation 6d9f0767-2501-40d5-80bd-fc33038c4e7d · inbound

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning cites this paper.

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning Federated Mutual Learning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T04:12:57.883322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:57.883322Z digest=sha256:15587a2752322dbe46752c95f4f88d6b81688f138a1dfd1fa65fd2a405b0f3ac

Observation 0b5fcd88-f0dd-4b24-b3c8-4d54a9a96d96 · inbound

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark cites this paper.

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark Federated Mutual Learning

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T10:55:15.038994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:55:15.038994Z digest=sha256:c5da7ece952a1108d2daee1af32f988cbe6069f6be070c533b9ba77990915869

Observation 24159cb1-0e1f-4e62-81af-10606aab1cba · inbound

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

Heterogeneous Federated Learning with Prototype Alignment and Upscaling Federated Mutual Learning

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T19:55:03.573369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:55:03.573369Z digest=sha256:af092b55d19c4d659aff1aa11aaf58f75d1e19296698da8d59c93d2afc0e5c91

Observation 5d864a07-255b-44c4-a23b-9a0acb88fbf5 · 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 Federated Mutual Learning

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T19:58:05.327855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:58:05.327855Z digest=sha256:58ed5bb8988165afec7fa573b924bab15e1a45d44266566250d880a84bc8e159

Observation 64f808df-e955-4269-aa97-0e4a01210bf2 · inbound

H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity cites this paper.

H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity Federated Mutual Learning

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T11:37:42.411641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:37:42.411641Z digest=sha256:d012879453c798e93dd1f980bad30dd747fbbd9a9ead3bb14db6c769f6a03358

Observation 8780bad1-fb04-41f4-b24b-f18393737d0c · 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 Federated Mutual Learning

Reference 49

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:56:05.907360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T02:35:40.593397Z digest=sha256:c80c0fd2e2a0699a4c64d2c101c375002920f38a2655a30e96181808b939f8fa

Observation e30af74b-c4d3-4f3f-97bb-c9971afd6d94 · inbound

STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning cites this paper.

STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning Federated Mutual Learning

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:42:30.415302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-13T07:41:51.513934Z digest=sha256:7d50fd116b0d802efe268f5473b2b29b76bc08925ecdd8cb16faaff7c8e9b3c2

Observation 2986ac0d-7d1a-481a-aee5-27e43e15f2ee · inbound

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

FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning Federated Mutual Learning

Reference 26

Resolution
unresolved
no resolver link, observed 2026-07-30T20:41:07.725136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T20:41:07.725136Z digest=sha256:c41f02856c2620a6b2b8f1094402088da87e848bef3317ad7b769e5983cb8363

Observation 353b81b6-5ae0-4121-8126-016fc10ea2c0 · 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 Federated Mutual Learning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T21:03:55.017965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T21:03:55.017965Z digest=sha256:75ad0cf849a2a25adce441d7431e8a3a6b23893d47d0ad1c88fcdbc7739d1db1