系统梳理联邦学习架构的核心原则与关键组件。
Principles and Components of Federated Learning Architectures
- 从五个维度解析联邦学习架构设计原理。
- 揭示系统异构性、数据划分等核心挑战。
- 适合研究者和工程实践者参考架构模式。
联邦学习（FL）是一种机器学习框架，多个客户端（如移动设备到企业）在中央服务器协调下协作构建模型，同时保持训练数据的去中心化特性。这种去中心化训练方式带来成本降低、隐私增强、安全提升及合规性优势。然而，联邦学习仍面临传统机器学习方法的局限性。本文详述联邦学习架构中的核心概念与特征，涵盖五大关键领域：系统异构性、数据划分、机器学习模型、通信协议与隐私技术。文章还指出现有研究的不足，并提出未来研究方向。此外，基于文献系统综述，本文归纳了若干联邦学习系统架构模式，有助于深入理解联邦学习的基本原理与具体实现细节。
原文摘要 · Abstract (English)
Federated Learning (FL) is a machine learning framework where multiple clients, from mobiles to enterprises, collaboratively construct a model under the orchestration of a central server but still retain the decentralized nature of the training data. This decentralized training of models offers numerous advantages, including cost savings, enhanced privacy, improved security, and compliance with legal requirements. However, for all its apparent advantages, FL is not immune to the limitations of conventional machine learning methodologies. This article provides an elaborate explanation of the inherent concepts and features found within federated learning architecture, addressing five key domains: system heterogeneity, data partitioning, machine learning models, communication protocols, and privacy techniques. This article also highlights the limitations in this domain and proposes avenues for future work. Besides, we provide a set of architectural patterns for federated learning systems, which are derived from the systematic survey of the literature. The main elements of FL, the fundamentals of Federated Learning, and a few architectural specifics will all be better understood with the aid of this research.
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