将联邦学习建模为多方博弈，实现低通信量下的均衡求解。
Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
- 把客户端视为博弈玩家，各自优化自身目标
- 新算法在随机环境下通信量更少仍能逼近均衡
- 适合研究理性客户端的联邦学习场景
传统联邦学习假设客户端目标一致并协作训练全局模型，但在现实场景中，客户端往往具有独立目标和策略行为。为此，本文提出多玩家联邦学习（MpFL）框架，将客户端建模为博弈论中的参与者，以达成均衡状态。在此设定下，每个参与者试图最大化自身效用函数，可能与整体目标不一致。我们提出每玩家本地随机梯度下降（PEARL-SGD）算法，各客户端独立进行本地更新，并周期性与其他玩家通信。理论上分析表明，在随机设置下，该算法相较于非本地版本能在更少通信量条件下收敛至均衡邻域。数值实验验证了理论结果。
原文摘要 · Abstract (English)
Traditional Federated Learning (FL) approaches assume collaborative clients with aligned objectives working towards a shared global model. However, in many real-world scenarios, clients act as rational players with individual objectives and strategic behaviors, a concept that existing FL frameworks are not equipped to adequately address. To bridge this gap, we introduce Multiplayer Federated Learning (MpFL), a novel framework that models the clients in the FL environment as players in a game-theoretic context, aiming to reach an equilibrium. In this scenario, each player tries to optimize their own utility function, which may not align with the collective goal. Within MpFL, we propose Per-Player Local Stochastic Gradient Descent (PEARL-SGD), an algorithm in which each player/client performs local updates independently and periodically communicates with other players. We theoretically analyze PEARL-SGD and prove that it reaches a neighborhood of equilibrium with less communication in the stochastic setup compared to its non-local counterpart. Finally, we verify our theoretical findings through numerical experiments.
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