mlsanityData to 7 Oct 2026

Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning

arXiv 2410.19321 ↗First posted 25 Oct 2024cs.GT cs.LGcited by 9

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Abstract (arXiv)

Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their data complementarity. In cross-silo FL, organizations that engage in business activities are key sources of FL-PTs. The resulting FL ecosystem has two features: (i) self-interest, and (ii) competition among FL-PTs. This requires the desirable FL-PT selection strategy to simultaneously mitigate the problems of free riders and conflicts of interest among competitors. To this end, we propose an optimal FL collaboration formation strategy -- FedEgoists -- which ensures that: (1) a FL-PT can benefit from FL if and only if it benefits the FL ecosystem, and (2) a FL-PT will not contribute to its competitors or their supporters. It provides an efficient clustering solution to group FL-PTs into coalitions, ensuring that within each coalition, FL-PTs share the same interest. We theoretically prove that the FL-PT coalitions formed are optimal since no coalitions can collaborate together to improve the utility of any of their members. Extensive experiments on widely adopted benchmark datasets demonstrate the effectiveness of FedEgoists compared to nine state-of-the-art baseline methods, and its ability to establish efficient collaborative networks in cross-silos FL with FL-PTs that engage in business activities.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 31 of its 61 references that are in the map. First posted on arXiv 25 Oct 2024.

Builds on

31 of 61 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Layer Normalizationcited by 5,579
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  7. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  9. 2021Towards Personalized Federated Learningcited by 462
  10. 2021Personalized Federated Learning using Hypernetworkscited by 210
  11. 2021Federated Learning for Open Bankingcited by 138
  12. 2020Learning the Pareto Front with Hypernetworkscited by 122
19 more
  1. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 121
  2. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 66
  3. 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
  4. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  5. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36
  6. 2020Byzantine-resilient Decentralized Stochastic Gradient Descentcited by 28
  7. 2021Learning Conjoint Attentions for Graph Neural Netscited by 26
  8. 2022Fairness in Federated Learning via Core-Stabilitycited by 22
  9. 2022Industry-Scale Orchestrated Federated Learning for Drug Discoverycited by 19
  10. 2021Collaboration Equilibrium in Federated Learningcited by 15
  11. 2021MarS-FL: Enabling Competitors to Collaborate in Federated Learningcited by 11
  12. 2022Towards the Practical Utility of Federated Learning in the Medical Domaincited by 9
  13. 2020Coopetition Against an Amazoncited by 6
  14. 2023Strategic Data Sharing between Competitorscited by 6
  15. 2023FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participantscited by 4
  16. 2024Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learningcited by 3
  17. 2020Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performancecited by 2
  18. 2019A Framework for Allocating Server Time to Spot and On-demand Services in Cloud Computingcited by 1
  19. 2023Federated Learning in Competitive EV Charging Marketcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 3.

9 papers.

  1. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  2. 2025Personalized Federated Learning via Learning Dynamic Graphscited by 2
  3. 2025A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learningcited by 1
  4. 2025Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learningcited by 0
  5. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  6. 2025Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learningcited by 0
  7. 2026Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learningcited by 0
  8. 2026Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approachcited by 0
  9. 2026FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgettingcited by 0