论文标题
对隐私保护的联合学习的评论
A Review of Privacy-preserving Federated Learning for the Internet-of-Things
论文作者
论文摘要
The Internet（IoT）生成了大量数据，其中很多归因于个人的活动和行为。在中央位置收集个人数据并在此数据上执行机器学习任务，给个人带来了重要的隐私风险，以及将这些数据传达给云的挑战。但是，基于机器学习，尤其是深度学习的分析从大量数据中受益匪浅，以开发高性能预测模型。这项工作将联合学习作为在分布式数据上执行机器学习的一种方法，目的是保护用户生成的数据的隐私以及降低与数据传输相关的通信成本。我们调查了各种各样的论文，涵盖了沟通效率，客户异质性和隐私保存方法，这些方法对于在物联网背景下对于联合学习至关重要。在整个评论中，我们确定了应用于联邦学习的不同方法的优势和劣势，最后，我们概述了将来保存联合学习研究的隐私方向，尤其是专注于物联网应用程序。
The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learning tasks on this data in a central location presents a significant privacy risk to individuals as well as challenges with communicating this data to the cloud. However, analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high-performance predictive models. This work reviews federated learning as an approach for performing machine learning on distributed data with the goal of protecting the privacy of user-generated data as well as reducing communication costs associated with data transfer. We survey a wide variety of papers covering communication-efficiency, client heterogeneity and privacy preserving methods that are crucial for federated learning in the context of the IoT. Throughout this review, we identify the strengths and weaknesses of different methods applied to federated learning and finally, we outline future directions for privacy preserving federated learning research, particularly focusing on IoT applications.
