Paper Citation Record · LEDGER

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning

As of 12 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2502.08829.

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

pith.paper-citation-record.v1
2502.08829 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:36:53.818667Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:32:12.793003Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T21:32:13.401489Z

Reference resolution

50 of 50 outbound references displayed

verified exact2
verified fuzzy17
unresolved30
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 774dfc50-83db-41c9-9316-f94edad832e5 · outbound

This paper cites Federated Learning with Personalization Layers.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated Learning with Personalization Layers

Reference 1

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Observation 9422b5e1-68be-46f0-90c1-3b557ed21384 · outbound

This paper cites Federated learning with hierarchical clustering of local updates to improve training on non-iid data.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated learning with hierarchical clustering of local updates to improve training on non-iid data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.228441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 759940c4-f894-4115-ad75-8b6156a169b9 · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning LEAF: A Benchmark for Federated Settings

Reference 3

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Observation aecae38f-fa0c-4aaa-b8d8-704ff0392d96 · outbound

This paper cites Entropy-sgd: Biasing gradient descent into wide valleys.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Entropy-sgd: Biasing gradient descent into wide valleys

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T23:36:53.670049Z

Source-reported events for the cited work

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Observation 6856e49f-b71a-4331-a1d4-0848414ba72b · outbound

This paper cites Shallowing deep networks: Layer-wise pruning based on feature representations.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Shallowing deep networks: Layer-wise pruning based on feature representations

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.214373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 23c7a5ba-0a0a-443d-a9d6-097cc9bab451 · outbound

This paper cites Maximizing Global Model Appeal in Federated Learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Maximizing Global Model Appeal in Federated Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T23:36:53.677220Z

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Observation fa278f13-5380-4e67-bdd5-b6c22681fe14 · outbound

This paper cites Exploiting shared representations for personalized federated learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Exploiting shared representations for personalized federated learning

Reference 7

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Observation 7571b7f9-fc30-465e-8055-217357ddc67b · outbound

This paper cites Federated learning for predicting clinical outcomes in patients with covid-19.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated learning for predicting clinical outcomes in patients with covid-19

Reference 8

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Observation 07268093-f785-4ce2-a058-114c226a3c23 · outbound

This paper cites Statistical comparisons of classifiers over multiple data sets.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Statistical comparisons of classifiers over multiple data sets

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.195149Z

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Observation c689d801-e7a4-4533-b415-869fa4519702 · outbound

This paper cites A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization

Reference 10

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Source-reported events for the cited work

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Observation 8b189b70-910f-4d30-a0c0-1bc81ae0bea4 · outbound

This paper cites New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:36:53.958891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9b13d847-cc1f-4ba3-b57c-ce6f6e49b1bb · outbound

This paper cites Learning to prune deep neural networks via layer-wise optimal brain surgeon.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Learning to prune deep neural networks via layer-wise optimal brain surgeon

Reference 12

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Source-reported events for the cited work

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Observation e22fe6e6-356a-4f25-82a9-a75363d0a275 · outbound

This paper cites Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.180678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 61532007-20dd-46f1-afe2-7c711b7906a0 · outbound

This paper cites Layer-wise Model Pruning based on Mutual Information.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Layer-wise Model Pruning based on Mutual Information

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T23:36:53.945588Z

Source-reported events for the cited work

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Observation e78d0565-e73a-49ee-85f6-276b0fa1ee69 · outbound

This paper cites Learning both weights and connections for efficient neural network.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Learning both weights and connections for efficient neural network

Reference 15

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Source-reported events for the cited work

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Observation dac946a5-c324-467c-a972-1e4d6bec6dab · outbound

This paper cites Cross-Silo Federated Learning: Challenges and Opportunities.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Cross-Silo Federated Learning: Challenges and Opportunities

Reference 16

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Observation a3778d96-aa0b-47b2-b6a2-3f7e76d3a5a5 · outbound

This paper cites Fantastic Generalization Measures and Where to Find Them.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Fantastic Generalization Measures and Where to Find Them

Reference 17

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Observation ab2cc2b1-9b80-4254-b5bd-b2076551d162 · outbound

This paper cites Mimic-iii, a freely accessible critical care database.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Mimic-iii, a freely accessible critical care database

Reference 18

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Source-reported events for the cited work

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Observation 532255cd-af7b-4dfd-9a4e-06021098813f · outbound

This paper cites Advances and open problems in federated learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Advances and open problems in federated learning

Reference 19

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Observation 1db8ee68-96b0-41aa-808e-0c1ce61c1b1d · outbound

This paper cites Similarity of neural network representations revisited.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Similarity of neural network representations revisited

Reference 20

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Source-reported events for the cited work

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Observation 3401cd63-3d9d-425b-86c0-1c6240c8fa09 · outbound

This paper cites Optimal brain damage.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Optimal brain damage

Reference 21

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Source-reported events for the cited work

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Observation 4cc4785e-56c5-43b0-a4cd-a6072385674a · outbound

This paper cites Layer-wise adaptive model aggregation for scalable federated learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Layer-wise adaptive model aggregation for scalable federated learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.142887Z

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Observation 633220b3-569e-466d-905d-8378e3d8623c · outbound

This paper cites Federated learning on non-iid data silos: An experimental study.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated learning on non-iid data silos: An experimental study

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.134033Z

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Observation 66c1028d-7206-4a3e-8fa7-057299d84b31 · outbound

This paper cites Federated optimization in heterogeneous networks.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated optimization in heterogeneous networks

Reference 24

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Observation 93a670fe-77a5-4e8d-be62-622943fd696b · outbound

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

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Ditto: Fair and robust federated learning through personalization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.120226Z

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Observation e3ba4703-c581-4a38-90f5-411891e04a61 · outbound

This paper cites Convergent Learning: Do different neural networks learn the same representations?.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Convergent Learning: Do different neural networks learn the same representations?

Reference 26

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Observation d2034eb1-feee-4019-92b5-915cc7cb3787 · outbound

This paper cites Learning efficient convolutional networks through network slimming.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Learning efficient convolutional networks through network slimming

Reference 27

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Observation 56ff05b4-2e3a-483b-a663-5413d35fd6e3 · outbound

This paper cites Rethinking the Value of Network Pruning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Rethinking the Value of Network Pruning

Reference 28

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Observation 64235214-5a09-4437-b5be-3f290be1926d · outbound

This paper cites Layer-wised model aggregation for personalized federated learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Layer-wised model aggregation for personalized federated learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.106342Z

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Observation 2a561f5e-f379-4559-8704-bf522f5e4d7e · outbound

This paper cites Understanding deep convolutional networks.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Understanding deep convolutional networks

Reference 30

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Observation b1256a1d-8066-47f0-8b59-519742a3dce9 · outbound

This paper cites Three Approaches for Personalization with Applications to Federated Learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Three Approaches for Personalization with Applications to Federated Learning

Reference 31

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Observation 8577d73e-001a-47fc-82dc-df594431de4c · outbound

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

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Communication- efficient learning of deep networks from decentralized data

Reference 32

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Observation c97bfc0e-f96f-43f5-b676-ad9fac40796b · outbound

This paper cites Importance estimation for neural network pruning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Importance estimation for neural network pruning

Reference 33

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Observation 9d18b2c4-c709-47b2-b686-ce481f016c7b · outbound

This paper cites Insights on representational similarity in neural networks with canonical correlation.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Insights on representational similarity in neural networks with canonical correlation

Reference 34

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Observation 87056410-1459-45be-92f3-393993b7c96b · outbound

This paper cites FedBABU: Towards Enhanced Representation for Federated Image Classification.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning FedBABU: Towards Enhanced Representation for Federated Image Classification

Reference 35

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This paper cites The Federated Tumor Segmentation (FeTS) Challenge.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning The Federated Tumor Segmentation (FeTS) Challenge

Reference 36

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Observation e3aa18f1-b982-453e-98b3-faa2cc71f7ae · outbound

This paper cites Federated learning enables big data for rare cancer boundary detection.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated learning enables big data for rare cancer boundary detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.068726Z

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Observation a4064a12-b823-44b4-92ad-17344ef9ad8a · outbound

This paper cites Pruning algorithms-a survey.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Pruning algorithms-a survey

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.059494Z

Source-reported events for the cited work

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Observation 06f94a2f-31b3-41db-a568-5f19cf5f959e · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 39

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Source-reported events for the cited work

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Observation 8556f1d0-6010-4087-aa9e-400a6cf14eb5 · outbound

This paper cites Federated multi-task learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated multi-task learning

Reference 40

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Observation 93446250-4b13-4cf4-986d-8f7e11af0a1e · outbound

This paper cites Personalized federated learning with moreau envelopes.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Personalized federated learning with moreau envelopes

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.045578Z

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Observation 1b6b57a1-75f9-4f02-9e9f-155e03d82c78 · outbound

This paper cites FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T23:36:53.792366Z

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Observation f4480df2-3305-4b32-9b4b-f27cc7b7f820 · outbound

This paper cites Towards personalized federated learning via heterogeneous model reassembly.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Towards personalized federated learning via heterogeneous model reassembly

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.036463Z

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Observation b92c4762-b798-453a-af66-cc0263b2b2e9 · outbound

This paper cites Towards understanding learning representations: To what extent do different neural networks learn the same representation.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Towards understanding learning representations: To what extent do different neural networks learn the same representation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.027012Z

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Observation 30df154d-d7c2-4a0a-a43d-01272eb75b13 · outbound

This paper cites Generalized shape metrics on neural representations.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Generalized shape metrics on neural representations

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:36:54.017772Z

Source-reported events for the cited work

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Observation 334dfa1d-7ca0-4cf3-ac50-d65d38838fdf · outbound

This paper cites How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014

Reference 46

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Observation a73aeed1-4d31-4641-a0b6-f139ee9d74ee · outbound

This paper cites Salvaging Federated Learning by Local Adaptation.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Salvaging Federated Learning by Local Adaptation

Reference 47

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Source-reported events for the cited work

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This paper cites Visualizing and understanding convolutional networks.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Visualizing and understanding convolutional networks

Reference 48

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Source-reported events for the cited work

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Observation 3bf3ac74-f275-45e8-8bba-f14c44216279 · outbound

This paper cites Federated Learning with Non-IID Data.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Federated Learning with Non-IID Data

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T23:36:53.815320Z

Source-reported events for the cited work

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Observation 732daae1-544d-41c4-9102-d71e18b0563b · outbound

This paper cites Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T23:36:53.998737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation bc74f504-2444-4957-ae96-facd8faa6646 · inbound

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning cites this paper.

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-11T21:32:13.406629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.