PAGE: Equilibrate Personalization and Generalization in Federated Learning
This addresses the problem of data heterogeneity in federated learning for service providers and clients, offering a novel approach to simultaneously optimize personalization and generalization, though it is incremental in combining existing techniques.
The paper tackles the challenge of balancing model personalization and generalization in federated learning by proposing PAGE, a game theory-based algorithm that uses reinforcement learning to find an equilibrium, resulting in improvements of up to 35.20% in global accuracy and 39.91% in local accuracy across four datasets.
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.