阅读:具有细粒度层聚合和分散聚类的个性化联邦学习框架

IF 9.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Haoyu Fu;Fengsen Tian;Guoqiang Deng;Lingyu Liang;Xinglin Zhang
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引用次数: 0

摘要

本地数据和客户端性能的异构性,以及现实世界的系统风险,正在推动联邦学习(FL)向个性化、模型异构和分散的方向发展。然而,由于异构模型的结构不同,难以识别具有相似数据分布的客户端,也难以进一步增强局部模型的个性化。因此,如何处理数据异构性,在解决模型异构性和系统风险的同时,为客户提供优质的个性化本地模型,是一个具有挑战性的问题。在本文中,我们提出了一个具有细粒度层聚合和分散聚类(${\sf Reads}$)的新颖个性化FL框架,该框架集成了四个关键组件:(1)基于隐私保障的深度互学习,用于模型训练和隐私保护;(2)异构模型层间的细粒度层相似度计算;(3)基于层相似度的完全去中心化聚类,用于客户端软聚类;(4)个性化层聚合,用于从其他客户端获取共同知识。通过${\sf Reads}$,客户可以获得适应模型异构性的个性化模型,而系统可以确保针对单点故障的鲁棒性。大量的实验证明了${\sf Reads}$在实现这些目标方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reads: A Personalized Federated Learning Framework With Fine-Grained Layer Aggregation and Decentralized Clustering
The heterogeneity of local data and client performance, along with real-world system risks, is driving the evolution of federated learning (FL) towards personalized, model-heterogeneous, and decentralized approaches. However, due to the differing structures of heterogeneous models, it is hard to use them to identify clients with similar data distributions and further enhance the personalization of local models. Therefore, how to deal with data heterogeneity to obtain superior personalized local models for clients, while simultaneously addressing model heterogeneity and system risks is a challenging problem. In this paper, we propose a novel personalized FL framework with fine-gRained layEr aggregAtion and Decentralized cluStering (${\sf Reads}$), which integrates four key components: (1) deep mutual learning with privacy guarantee for model training and privacy preservation, (2) fine-grained layer similarity computation among heterogeneous model layers, (3) fully decentralized clustering for soft clustering of clients based on layer similarities, and (4) personalized layer aggregation for capturing common knowledge from other clients. Through ${\sf Reads}$, clients obtain personalized models that accommodate model heterogeneity, while the system ensures robustness against a single point of failure. Extensive experiments demonstrate the efficacy of ${\sf Reads}$ in achieving these goals.
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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