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Defection-Free Collaboration between Competitors in a Learning System

arXiv 2406.15898 ↗First posted 22 Jun 2024cs.GT cs.LGcited by 0

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Abstract (arXiv)

We study collaborative learning systems in which the participants are competitors who will defect from the system if they lose revenue by collaborating. As such, we frame the system as a duopoly of competitive firms who are each engaged in training machine-learning models and selling their predictions to a market of consumers. We first examine a fully collaborative scheme in which both firms share their models with each other and show that this leads to a market collapse with the revenues of both firms going to zero. We next show that one-sided collaboration in which only the firm with the lower-quality model shares improves the revenue of both firms. Finally, we propose a more equitable, *defection-free* scheme in which both firms share with each other while losing no revenue, and we show that our algorithm converges to the Nash bargaining solution.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 22 Jun 2024.

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14 of 22 references.

References · 14

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  4. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  5. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  6. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  7. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  8. 2020Model-sharing Games: Analyzing Federated Learning Under Voluntary Participationcited by 60
  9. 2021One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learningcited by 39
  10. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  11. 2021MarS-FL: Enabling Competitors to Collaborate in Federated Learningcited by 11
  12. 2023Provably Personalized and Robust Federated Learningcited by 11
2 more
  1. 2023Strategic Data Sharing between Competitorscited by 6
  2. 2023On the Effect of Defections in Federated Learning and How to Prevent Themcited by 4

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