From Distributed Machine Learning to Federated Learning: A Survey
作者:Ji Liu, Jizhou Huang, Yang Zhou, Xuhong Li, Shilei Ji, Haoyi Xiong & Dejing Dou
Abstract: In recent years, data and computing resources are typically distributed
in the devices of end users, various regions or organizations. Because of laws or
regulations, the distributed data and computing resources cannot be aggregated
or directly shared among different regions or organizations for machine learning
tasks. Federated learning emerges as an efficient approach to exploit distributed
data and computing resources, so as to collaboratively train machine learning
models. At the same time, federated learning obeys the laws and regulations and
ensures data security and data privacy. In this paper, we provide a comprehensive
survey of existing works for federated learning. First, we propose a functional
architecture of federated learning systems and a taxonomy of related techniques.
Second, we explain the federated learning systems from four aspects: diverse types
of parallelism, aggregation algorithms, data communication, and the security of
federated learning systems. Third, we present four widely used federated systems
based on the functional architecture. Finally, we summarize the limitations and
propose future research directions.
Keywords: Federated learning , Distributed system , Parallel computing ,Security, Privacy
摘要：近年来，数据和计算资源通常分布在终端用户、各个区域或组织的设备中。由于法律或法规的限制，分布式数据和计算资源无法在不同地区或组织之间聚合或直接共享，用于机器学习任务。联邦学习是一种利用分布式数据和计算资源，协同训练机器学习模型的有效方法。同时，联邦学习遵守法律法规，保证数据安全和数据隐私。在本文中，我们对联邦学习的现有工作进行了全面的综述。首先，我们提出了联邦学习系统的功能架构和相关技术的分类。其次，从不同类型的并行性、聚合算法、数据通信、联邦学习系统的安全性4个方面对联邦学习系统进行说明。第三，在功能架构的基础上，提出了4种应用广泛的联邦系统。最后，总结本文的局限性并提出未来的研究方向。
关键字：联邦学习,分布式系统,并行计算,安全,隐私
DOI：10.1007/s10115-022-01664-x
全文链接：https://arxiv.org/pdf/2104.14362v4.pdf
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