PAGE: Equilibrate Personalization and Generalization in Federated Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Harmonizing Generalization and Personalization in Federated Prompt Learning
Federated Prompt Learning (FPL) incorporates large pre-trained Vision-Language models (VLM) into federated learning through prompt tuning. The transferable representations and remarkable generalization capacity of VLM ma…
Federated LearningPrompt LearningPrivacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models
Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently pro…
Federated LearningPrivacy PreservingPrompt LearningAddressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
Federated learning is an efficient framework designed to facilitate collaborative model training across multiple distributed devices while preserving user data privacy. A significant challenge of federated learning is da…
Federated LearningFederated Multi-armed Bandits with Personalization
A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed, which is a new bandit paradigm analogous to the federated learning (FL) framework in supervised learning and enjoys the features of …
Federated LearningMulti-Armed BanditsFederated Variational Inference: Towards Improved Personalization and Generalization
Conventional federated learning algorithms train a single global model by leveraging all participating clients' data. However, due to heterogeneity in client generative distributions and predictive models, these approach…
Bayesian InferenceFederated LearningGeneralization Boundsimage-classification+2