mlsanityData to 7 Oct 2026

MarS-FL: Enabling Competitors to Collaborate in Federated Learning

arXiv 2110.13464 ↗First posted 26 Oct 2021cs.LG cs.GTcited by 11

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Abstract (arXiv)

Federated learning (FL) is rapidly gaining popularity and enables multiple data owners (em a.k.a. FL participants) to collaboratively train machine learning models in a privacy-preserving way. A key unaddressed scenario is that these FL participants are in a competitive market, where market shares represent their competitiveness. Although they are interested to enhance the performance of their respective models through FL, market leaders (who are often data owners who can contribute significantly to building high performance FL models) want to avoid losing their market shares by enhancing their competitors' models. Currently, there is no modeling tool to analyze such scenarios and support informed decision-making. In this paper, we bridge this gap by proposing the underlinemarket underlineshare-based decision support framework for participation in underlineFL (MarS-FL). We introduce em two notions of δ-stable market and em friendliness to measure the viability of FL and the market acceptability of FL. The FL participants' behaviours can then be predicted using game theoretic tools (i.e., their optimal strategies concerning participation in FL). If the market δ-stability is achievable, the final model performance improvement of each FL-PT shall be bounded, which relates to the market conditions of FL applications. We provide tight bounds and quantify the friendliness, kappa, of given market conditions to FL. Experimental results show the viability of FL in a wide range of market conditions. Our results are useful for identifying the market conditions under which collaborative FL model training is viable among competitors, and the requirements that have to be imposed while applying FL under these conditions.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 26 Oct 2021.

Builds on

10 of 33 references.

References · 10

  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. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  6. 2021Federated Learning for Open Bankingcited by 138
  7. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  8. 2020Collaborative Fairness in Federated Learningcited by 95
  9. 2020FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MECcited by 48
  10. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 3; 2025: 2; 2026: 1.

11 papers.

  1. 2024Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learningcited by 9
  2. 2023Strategic Data Sharing between Competitorscited by 6
  3. 2023Trustworthy Federated Learning: A Surveycited by 4
  4. 2023FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participantscited by 4
  5. 2024Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learningcited by 3
  6. 2023A Framework for Incentivized Collaborative Learningcited by 2
  7. 2025A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learningcited by 1
  8. 2023Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approachcited by 0
  9. 2024Defection-Free Collaboration between Competitors in a Learning Systemcited by 0
  10. 2025Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learningcited by 0
  11. 2026Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learningcited by 0