mlsanityData to 7 Oct 2026

Collaboration Equilibrium in Federated Learning

arXiv 2108.07926 ↗First posted 18 Aug 2021cs.LGcited by 15

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Abstract (arXiv)

Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy. Recently, there have been rising concerns on the distributional discrepancies across different clients, which could even cause counterproductive consequences when collaborating with others. While it is not necessarily that collaborating with all clients will achieve the best performance, in this paper, we study a rational collaboration called ``collaboration equilibrium'' (CE), where smaller collaboration coalitions are formed. Each client collaborates with certain members who maximally improve the model learning and isolates the others who make little contribution. We propose the concept of benefit graph which describes how each client can benefit from collaborating with other clients and advance a Pareto optimization approach to identify the optimal collaborators. Then we theoretically prove that we can reach a CE from the benefit graph through an iterative graph operation. Our framework provides a new way of setting up collaborations in a research network. Experiments on both synthetic and real world data sets are provided to demonstrate the effectiveness of our method.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 18 Aug 2021.

Builds on

27 of 45 references.

References · 27

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  4. 2018Taskonomy: Disentangling Task Transfer Learningcited by 631
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  6. 2019Federated Learning with Personalization Layerscited by 533
  7. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  8. 2019Agnostic Federated Learningcited by 451
  9. 2019Fair Resource Allocation in Federated Learningcited by 389
  10. 2019Which Tasks Should Be Learned Together in Multi-task Learning?cited by 367
  11. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  12. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
15 more
  1. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  2. 2018Characterizing and Avoiding Negative Transfercited by 226
  3. 2021Personalized Federated Learning using Hypernetworkscited by 210
  4. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  5. 2016UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memorycited by 200
  6. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  7. 2020Learning the Pareto Front with Hypernetworkscited by 122
  8. 2021Distributionally Robust Federated Averagingcited by 104
  9. 2020Robust Federated Learning: The Case of Affine Distribution Shiftscited by 102
  10. 2016Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classificationcited by 93
  11. 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
  12. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  13. 2021Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decompositioncited by 17
  14. 2020Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utilitycited by 9
  15. 2021A Wasserstein Minimax Framework for Mixed Linear Regressioncited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 4; 2025: 4; 2026: 2.

15 papers.

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  1. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  2. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  3. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  4. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  5. 2024Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learningcited by 9
  6. 2024Balancing Similarity and Complementarity for Federated Learningcited by 7
  7. 2024Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learningcited by 3
  8. 2023Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Datacited by 1
  9. 2025Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learningcited by 1
  10. 2024Adaptive Guidance for Local Training in Heterogeneous Federated Learningcited by 0
  11. 2025Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learningcited by 0
  12. 2025Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning Approachcited by 0
3 more
  1. 2025Incentivizing High-quality Participation From Federated Learning Agentscited by 0
  2. 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspectivecited by 0
  3. 2026Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddingscited by 0