mlsanityData to 7 Oct 2026

Optimizing the Collaboration Structure in Cross-Silo Federated Learning

arXiv 2306.06508 ↗First posted 10 Jun 2023cs.LGcited by 19

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Abstract (arXiv)

In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized. Through utilizing more training data, FL suffers from the potential negative transfer problem: the global FL model may even perform worse than the models trained with local data only. In this paper, we propose FedCollab, a novel FL framework that alleviates negative transfer by clustering clients into non-overlapping coalitions based on their distribution distances and data quantities. As a result, each client only collaborates with the clients having similar data distributions, and tends to collaborate with more clients when it has less data. We evaluate our framework with a variety of datasets, models, and types of non-IIDness. Our results demonstrate that FedCollab effectively mitigates negative transfer across a wide range of FL algorithms and consistently outperforms other clustered FL algorithms.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 24 of its 39 references that are in the map. First posted on arXiv 10 Jun 2023.

Builds on

24 of 39 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 987
  7. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  8. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  9. 2018LEAF: A Benchmark for Federated Settingscited by 780
  10. 2017Federated Multi-Task Learningcited by 669
  11. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  12. 2020Personalized Federated Learning with Moreau Envelopescited by 612
12 more
  1. 2020Federated Learning with Matched Averagingcited by 564
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  4. 2019Agnostic Federated Learningcited by 451
  5. 2019Fair Resource Allocation in Federated Learningcited by 389
  6. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  7. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  8. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  9. 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
  10. 2022Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspacescited by 36
  11. 2022On the Convergence of Clustered Federated Learningcited by 31
  12. 2020Continuous Transfer Learning with Label-informed Distribution Alignmentcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 6; 2025: 7; 2026: 1.

19 papers.

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  1. 2023Adaptive Test-Time Personalization for Federated Learningcited by 23
  2. 2023Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learningcited by 20
  3. 2023FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networkscited by 7
  4. 2024Hierarchical Federated Learning with Multi-Timescale Gradient Correctioncited by 7
  5. 2024Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clientscited by 5
  6. 2023Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologiescited by 3
  7. 2024Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradientscited by 3
  8. 2024Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learningcited by 3
  9. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  10. 2024Distributionally Robust Clustered Federated Learning: A Case Study in Healthcarecited by 2
  11. 2025UnifyFL: Enabling Decentralized Cross-Silo Federated Learningcited by 1
  12. 2023Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hullscited by 0
7 more
  1. 2024Personalized Additive Modeling for Multi-level Federated Learningcited by 0
  2. 2025Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Samplingcited by 0
  3. 2025FedAvgen: Metadata for Model Aggregation In Communication Systemscited by 0
  4. 2025Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learningcited by 0
  5. 2025Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitionscited by 0
  6. 2025Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learningcited by 0
  7. 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learningcited by 0