mlsanityData to 7 Oct 2026

Find Your Friends: Personalized Federated Learning with the Right Collaborators

arXiv 2210.06597 ↗First posted 12 Oct 2022cs.LGcited by 10

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Abstract (arXiv)

In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted central party that can coordinate the clients to ensure that each of them can benefit from others. To address these concerns, we present a novel decentralized framework, FedeRiCo, where each client can learn as much or as little from other clients as is optimal for its local data distribution. Based on expectation-maximization, FedeRiCo estimates the utilities of other participants' models on each client's data so that everyone can select the right collaborators for learning. As a result, our algorithm outperforms other federated, personalized, and/or decentralized approaches on several benchmark datasets, being the only approach that consistently performs better than training with local data only.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 12 Oct 2022.

Builds on

27 of 35 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,181
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2017Federated Multi-Task Learningcited by 669
  6. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  8. 2019Federated Learning with Personalization Layerscited by 533
  9. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  10. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  11. 2021Towards Personalized Federated Learningcited by 462
  12. 2020Adaptive Personalized Federated Learningcited by 353
15 more
  1. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  2. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  3. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  4. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  5. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  6. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  7. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  8. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  9. 2021Federated Learning for Open Bankingcited by 138
  10. 2020Federated Mutual Learningcited by 70
  11. 2019Variational Federated Multi-Task Learningcited by 69
  12. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 56
  13. 2020Decentralized Federated Learning via Mutual Knowledge Transfercited by 42
  14. 2022Personalized Federated Learning With Graphcited by 29
  15. 2021Collaboration Equilibrium in Federated Learningcited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 2; 2026: 1.

10 papers.

  1. 2024Personalized Collaborative Fine-Tuning for On-Device Large Language Modelscited by 9
  2. 2024Decentralized Directed Collaboration for Personalized Federated Learningcited by 9
  3. 2023Collaborative Learning via Prediction Consensuscited by 8
  4. 2023Efficient Node Selection in Private Personalized Decentralized Learningcited by 4
  5. 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Datacited by 3
  6. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  7. 2023Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approachcited by 1
  8. 2023Resource-constrained knowledge diffusion processes inspired by human peer learningcited by 0
  9. 2025S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learningcited by 0
  10. 2026SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learningcited by 0