提出贝叶斯框架解决联邦学习中的数据分布不均问题
A Bayesian Framework for Clustered Federated Learning
- 用贝叶斯方法动态分配客户端到聚类，避免固定归属
- 实验显示该方法在多种场景下提升模型性能
- 适合处理异构数据的联邦学习系统设计者
联邦学习面临的主要挑战之一是客户端数据非独立同分布（non-IID），这通常由数据集不平衡及客户端使用不同数据源导致。知识共享与模型个性化是应对该问题的关键策略。聚类联邦学习通过将具有相似数据分布的客户端分组，使每个客户端与其所属聚类内的同伴共同训练特定分布的模型。本文提出一个统一的贝叶斯框架来关联客户端与聚类，并设计了若干实用算法，以在性能与计算复杂度之间权衡不断增长的数据关联。该工作揭示了客户端-聚类关联机制，实现了新的知识共享方式。所提框架无需强求唯一客户端-聚类绑定，实验证明此设计能有效提升模型表现。
原文摘要 · Abstract (English)
One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced datasets and use of different data sources across clients. Knowledge sharing and model personalization are key strategies for addressing this issue. Clustered federated learning is a class of FL methods that groups clients that observe similarly distributed data into clusters, such that every client is typically associated with one data distribution and participates in training a model for that distribution along their cluster peers. In this paper, we present a unified Bayesian framework for clustered FL which associates clients to clusters. Then we propose several practical algorithms to handle the, otherwise growing, data associations in a way that trades off performance and computational complexity. This work provides insights on client-cluster associations and enables client knowledge sharing in new ways. The proposed framework circumvents the need for unique client-cluster associations, which is seen to increase the performance of the resulting models in a variety of experiments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。