A Multi-player Game for Studying Federated Learning Incentive Schemes

Kang Loon Ng; Zichen Chen; Zelei Liu; Han Yu; Yang Liu; Qiang Yang

IJCAI 2020poster0 citations

A Multi-player Game for Studying Federated Learning Incentive Schemes

Kang Loon Ng, Zichen Chen, Zelei Liu, Han Yu, Yang Liu, Qiang Yang

Abstract

Federated Learning (FL) enables participants to "share'' their sensitive local data in a privacy preserving manner and collaboratively build machine learning models. In order to sustain long-term participation by high quality data owners (especially if they are businesses), FL systems need to provide suitable incentives. To design an effective incentive scheme, it is important to understand how FL participants respond under such schemes. This paper proposes FedGame, a multi-player game to study how FL participants make action selection decisions under different incentive schemes. It allows human players to role-play under various conditions. The decision-making processes can be analyzed and visualized to inform FL incentive mechanism design in the future.

Machine Learning: generalGame Playing: generalHuman-Computer Interactive Systems: general

BibTeX

@inproceedings{ijcai2020p769,
  title     = {A Multi-player Game for Studying Federated Learning Incentive Schemes},
  author    = {Ng, Kang Loon and Chen, Zichen and Liu, Zelei and Yu, Han and Liu, Yang and Yang, Qiang},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Christian Bessiere},
  pages     = {5279--5281},
  year      = {2020},
  month     = {7},
  note      = {Demos},
  doi       = {10.24963/ijcai.2020/769},
  url       = {https://doi.org/10.24963/ijcai.2020/769},
}