Defection-Free Collaboration between Competitors in a Learning System
<|reference_start|>Defection-Free Collaboration between Competitors in a Learning System: 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.<|reference_end|>
@article{werner2024defection-free,
    title={Defection-Free Collaboration between Competitors in a Learning System},
    author={Mariel Werner, Sai Praneeth Karimireddy, Michael I. Jordan},
    journal={arXiv preprint arXiv:2406.15898},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15898},
    primaryClass={cs.GT cs.LG}
}