Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server). Representing clients' current demands and the server's future demand, local model personalization and global model generalization are separately investigated, as the ill-effects of data heterogeneity enforce the community to focus on one over the other. However, these two seemingly competing goals are of equal importance rather than black and white issues, and should be achieved simultaneously. In this paper, we propose the first algorithm to balance personalization and generalization on top of game theory, dubbed PAGE, which reshapes FL as a co-opetition game between clients and the server. To explore the equilibrium, PAGE further formulates the game as Markov decision processes, and leverages the reinforcement learning algorithm, which simplifies the solving complexity. Extensive experiments on four widespread datasets show that PAGE outperforms state-of-the-art FL baselines in terms of global and local prediction accuracy simultaneously, and the accuracy can be improved by up to 35.20% and 39.91%, respectively. In addition, biased variants of PAGE imply promising adaptiveness to demand shifts in practice.

翻译：联邦学习（FL）正成为机器学习即服务的重要推动力，客户（客户端）在服务提供商（服务器）的协调下通过共享本地更新实现协作收益。针对数据异质性导致的负面影响使得学界更关注某一目标，本地模型个性化与全局模型泛化分别代表了客户当前需求与服务器未来需求，这两者被独立研究。然而这两个看似竞争的目标并非非黑即白的问题，而是同等重要且应同时实现。本文提出首个基于博弈论平衡个性化与泛化的算法PAGE，将联邦学习重塑为客户端与服务器之间的合作竞争博弈。为探索均衡解，PAGE进一步将该博弈形式化为马尔可夫决策过程，并采用强化学习算法简化求解复杂度。在四个广泛使用的数据集上的大量实验表明，PAGE在全局与局部预测准确率上同时超越现有最优联邦学习基线，准确率分别可提升35.20%和39.91%。此外，PAGE的有偏变体在实践需求变化中展现出良好的自适应潜力。