物联网隐私保护联邦学习研究综述
A Review of Privacy-preserving Federated Learning for the Internet-of-Things
摘要：
物联网（IoT）产生大量数据，其中大部分可归因于个人的活动和行为。收集个人数据并在中央位置对这些数据执行机器学习任务，会给个人带来重大的隐私风险，也会给将这些数据传输到云端带来挑战。然而，基于机器学习，特别是深度学习的分析可以从大量数据中受益匪浅，以开发高性能预测模型。这项工作回顾了联邦学习作为一种方法，用于对分布式数据执行机器学习，其目标是保护用户生成的数据的隐私，以及减少与数据传输相关的通信成本。我们调查了各种各样的论文，涵盖了通信效率，客户端异构性和隐私保护方法，这些方法对于物联网环境中的联邦学习至关重要。在这篇综述中，我们确定了应用于联邦学习的不同方法的优点和缺点，最后，我们概述了隐私保护联邦学习研究的未来方向，特别是专注于物联网应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
