Paper Citation Record · LEDGER

Federated Learning: Challenges, Methods, and Future Directions

As of 14 August 2026, this Paper Citation Record lists 100 of 141 outbound references and 3 inbound Pith citation observations for arXiv:1908.07873.

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

pith.paper-citation-record.v1
1908.07873 v1

Coverage vector

measured 100 of 141 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:57:36.313408Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:47:35.006814Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:47:18.791263Z

Reference resolution

100 of 141 outbound references displayed

verified exact6
verified fuzzy5
unresolved89
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b2955b72-0261-419a-a1f5-eddf9dcbc5c7 · outbound

This paper cites URL https://www.tensorflow.org/federated.

Federated Learning: Challenges, Methods, and Future Directions URL https://www.tensorflow.org/federated

Reference 1

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Observation 2989fb09-010d-41f2-938e-f444bcd7fcdd · outbound

This paper cites Abadi, A.

Federated Learning: Challenges, Methods, and Future Directions Abadi, A

Reference 2

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Observation 60389532-631d-4354-ad13-dfd06150bae4 · outbound

This paper cites Agarwal, A.

Federated Learning: Challenges, Methods, and Future Directions Agarwal, A

Reference 3

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Observation 9536e541-37c6-4e30-ae4e-18a7e1257fcc · outbound

This paper cites Agrawal and R.

Federated Learning: Challenges, Methods, and Future Directions Agrawal and R

Reference 4

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Observation d54f832f-9ae6-45b8-9464-6965d01ac978 · outbound

This paper cites Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System.

Federated Learning: Challenges, Methods, and Future Directions Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

Reference 5

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Observation 0ebe6acc-d922-4264-8c0a-7c2f8d5db218 · outbound

This paper cites Anguita, A.

Federated Learning: Challenges, Methods, and Future Directions Anguita, A

Reference 6

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Observation bc2a9531-e4f6-4048-a3d8-732f2daad60e · outbound

This paper cites Bassily, A.

Federated Learning: Challenges, Methods, and Future Directions Bassily, A

Reference 7

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Source-reported events for the cited work

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Observation 2914d106-4bde-431d-b43a-4084e449a710 · outbound

This paper cites Protection Against Reconstruction and Its Applications in Private Federated Learning.

Federated Learning: Challenges, Methods, and Future Directions Protection Against Reconstruction and Its Applications in Private Federated Learning

Reference 8

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Source-reported events for the cited work

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Observation 22b59ac6-ab5a-4dcc-a799-b6ef1f9c4ac4 · outbound

This paper cites Bolukbasi, J.

Federated Learning: Challenges, Methods, and Future Directions Bolukbasi, J

Reference 9

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Observation 6d09f09f-e283-42cc-aef2-1d2da2a85c47 · outbound

This paper cites Bonawitz, V.

Federated Learning: Challenges, Methods, and Future Directions Bonawitz, V

Reference 10

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Observation 1d40715e-ec2b-431d-ba57-01baca8c3de9 · outbound

This paper cites Bonawitz, H.

Federated Learning: Challenges, Methods, and Future Directions Bonawitz, H

Reference 11

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Source-reported events for the cited work

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Observation 0946d275-7b2a-4293-9776-773ddd827ba3 · outbound

This paper cites Bonomi, R.

Federated Learning: Challenges, Methods, and Future Directions Bonomi, R

Reference 12

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Observation 1a5af51f-51c9-4487-aa9f-8e0d43cd4909 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation 4a26df15-88d6-47cf-ac04-9ac4190b2921 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

Reference 14

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Source-reported events for the cited work

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Observation 4066ab26-852b-4a19-90ac-fc546294358c · outbound

This paper cites Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.

Federated Learning: Challenges, Methods, and Future Directions Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Reference 15

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Observation 4466b572-099a-425d-a2f2-d55025b52be8 · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

Federated Learning: Challenges, Methods, and Future Directions LEAF: A Benchmark for Federated Settings

Reference 16

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Observation 267d95bd-43df-413d-8b14-c225c992a54c · outbound

This paper cites The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks.

Federated Learning: Challenges, Methods, and Future Directions The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

Reference 17

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Observation 8fd2c13f-242f-484d-9984-cef2b7dd8304 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

Reference 18

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Source-reported events for the cited work

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Observation 424421b8-b852-487e-8dc2-691644bc282c · outbound

This paper cites Castro, B.

Federated Learning: Challenges, Methods, and Future Directions Castro, B

Reference 19

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Observation 43c3bc33-80a2-4e05-a699-c4a1b064a06c · outbound

This paper cites Charles and D.

Federated Learning: Challenges, Methods, and Future Directions Charles and D

Reference 20

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Observation d1afde38-0705-41bd-bec3-6cc42cca5f88 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

Reference 21

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Observation f8e81656-6e23-45c9-b49c-339368c0ffe3 · outbound

This paper cites Chaudhuri, C.

Federated Learning: Challenges, Methods, and Future Directions Chaudhuri, C

Reference 22

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Source-reported events for the cited work

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Observation d16485f8-8b9f-42e7-91da-02426aff5cca · outbound

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

Reference 23

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This paper cites Federated Meta-Learning with Fast Convergence and Efficient Communication.

Federated Learning: Challenges, Methods, and Future Directions Federated Meta-Learning with Fast Convergence and Efficient Communication

Reference 24

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Observation 0845d26c-0c23-44f0-85bb-5df1a80664e8 · outbound

This paper cites Secure Computation for Machine Learning With SPDZ.

Federated Learning: Challenges, Methods, and Future Directions Secure Computation for Machine Learning With SPDZ

Reference 25

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Observation 7c39854b-f47b-457b-9b7c-878d54ad1c80 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Variational Federated Multi-Task Learning

Reference 26

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Observation c9ba3d8a-3480-4107-8016-f88b50c8794d · outbound

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

Reference 27

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Observation a976053d-cfa0-4cd7-a65a-b7c62a1646ed · outbound

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Federated Learning: Challenges, Methods, and Future Directions Dekel, R

Reference 28

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Observation b6ad941b-760e-4b16-b14d-1b4caf983147 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Deshpande, C

Reference 29

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Observation 58cbd7cf-5827-4cb2-a7c8-545938b7e6e0 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Duchi, M

Reference 30

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

Reference 31

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Observation 0a1f68b8-0cb1-4126-862c-9dac0cd71fc5 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

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Observation ba503359-4e0b-4874-a87a-a775413d5f30 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Dwork and A

Reference 33

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Observation fe0f3ee5-2d96-4fe4-9c63-f8bb41b87905 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Dwork, F

Reference 34

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Federated Learning: Challenges, Methods, and Future Directions Eichner, T

Reference 35

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Federated Learning: Challenges, Methods, and Future Directions El Emam and F

Reference 36

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Federated Learning: Challenges, Methods, and Future Directions Evgeniou and M

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Federated Learning: Challenges, Methods, and Future Directions Feldman, I

Reference 38

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Federated Learning: Challenges, Methods, and Future Directions Fredrikson, S

Reference 39

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Federated Learning: Challenges, Methods, and Future Directions Garcia Lopez, A

Reference 40

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Federated Learning: Challenges, Methods, and Future Directions Differentially Private Federated Learning: A Client Level Perspective

Reference 41

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Federated Learning: Challenges, Methods, and Future Directions Scalable and Differentially Private Distributed Aggregation in the Shuffled Model

Reference 42

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Federated Learning: Challenges, Methods, and Future Directions Goryczka and L

Reference 43

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Federated Learning: Challenges, Methods, and Future Directions Model Aggregation via Good-Enough Model Spaces

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Federated Learning: Challenges, Methods, and Future Directions One-Shot Federated Learning

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Federated Learning: Challenges, Methods, and Future Directions Federated Learning for Mobile Keyboard Prediction

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

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Federated Learning: Challenges, Methods, and Future Directions Huang, F

Reference 50

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Federated Learning: Challenges, Methods, and Future Directions Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records

Reference 51

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Observation eac7a29c-71a8-4e82-8ea9-459b6bd5034c · outbound

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Federated Learning: Challenges, Methods, and Future Directions LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

Reference 52

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Federated Learning: Challenges, Methods, and Future Directions Iyengar, J

Reference 53

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Federated Learning: Challenges, Methods, and Future Directions Jaggi, V

Reference 54

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Federated Learning: Challenges, Methods, and Future Directions Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

Reference 55

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Federated Learning: Challenges, Methods, and Future Directions Jiang and G

Reference 56

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Federated Learning: Challenges, Methods, and Future Directions Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

Reference 57

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Observation 1e875a4d-10af-4327-8c2e-460bce95b5ea · outbound

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Federated Learning: Challenges, Methods, and Future Directions Adaptive Gradient-Based Meta-Learning Methods

Reference 58

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Observation 6a1a4b47-0c2a-4808-bb99-b31f7c9c488f · outbound

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Federated Learning: Challenges, Methods, and Future Directions Federated Learning: Strategies for Improving Communication Efficiency

Reference 59

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Federated Learning: Challenges, Methods, and Future Directions Kuﬂik, J

Reference 60

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Federated Learning: Challenges, Methods, and Future Directions Decentralized Bayesian Learning over Graphs

Reference 61

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Observation 71ca5ac6-3b14-4d0c-9b35-fa01d2137eb1 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Lee and D

Reference 62

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Federated Learning: Challenges, Methods, and Future Directions Federated Optimization in Heterogeneous Networks

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Federated Learning: Challenges, Methods, and Future Directions Fair Resource Allocation in Federated Learning

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Federated Learning: Challenges, Methods, and Future Directions Don't Use Large Mini-Batches, Use Local SGD

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Federated Learning: Challenges, Methods, and Future Directions Lindell and B

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Federated Learning: Challenges, Methods, and Future Directions Client-Edge-Cloud Hierarchical Federated Learning

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

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Federated Learning: Challenges, Methods, and Future Directions Melis, G

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Federated Learning: Challenges, Methods, and Future Directions Melis, C

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Federated Learning: Challenges, Methods, and Future Directions Mohassel and P

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Federated Learning: Challenges, Methods, and Future Directions Mohri, G

Reference 80

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Federated Learning: Challenges, Methods, and Future Directions Nikolaenko, U

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Federated Learning: Challenges, Methods, and Future Directions Nishio and R

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Federated Learning: Challenges, Methods, and Future Directions Pantelopoulos and N

Reference 84

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Federated Learning: Challenges, Methods, and Future Directions Papernot, M

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Federated Learning: Challenges, Methods, and Future Directions Papernot, S

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Reference 87

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

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Federated Learning: Challenges, Methods, and Future Directions Federated Learning for Emoji Prediction in a Mobile Keyboard

Reference 89

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Federated Learning: Challenges, Methods, and Future Directions Rastegari, V

Reference 90

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Federated Learning: Challenges, Methods, and Future Directions MLSys: The New Frontier of Machine Learning Systems

Reference 91

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Federated Learning: Challenges, Methods, and Future Directions Recht, C

Reference 92

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Federated Learning: Challenges, Methods, and Future Directions AIDE: Fast and Communication Efficient Distributed Optimization

Reference 93

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Federated Learning: Challenges, Methods, and Future Directions Reisizadeh, S

Reference 94

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Federated Learning: Challenges, Methods, and Future Directions Unresolved cited work

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Observation 8eaf8df7-17e5-4608-9bef-2afe44b07743 · outbound

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Federated Learning: Challenges, Methods, and Future Directions Samarakoon, M

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Federated Learning: Challenges, Methods, and Future Directions Robust and Communication-Efficient Federated Learning from Non-IID Data

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Federated Learning: Challenges, Methods, and Future Directions Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition

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Pith citing papers

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Adaptive Federated Optimization Federated Learning: Challenges, Methods, and Future Directions

Reference 225

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Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction Federated Learning: Challenges, Methods, and Future Directions

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TallyTrain: Communication-Efficient Federated Distillation Federated Learning: Challenges, Methods, and Future Directions

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