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From distributed machine learning to federated learning: In the view of data privacy and security
Concurrency and Computation: Practice and Experience ( IF 2 ) Pub Date : 2020-09-23 , DOI: 10.1002/cpe.6002
Sheng Shen 1 , Tianqing Zhu 1 , Di Wu 2 , Wei Wang 1 , Wanlei Zhou 1
Affiliation  

Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micromanaging the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a submodel locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multiparty computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.

中文翻译:

从分布式机器学习到联邦学习:从数据隐私和安全角度看

联邦学习是分布式机器学习的改进版本,它进一步减轻了通常由中央服务器执行的操作。服务器变得更像是协调客户一起工作的助手,而不是像传统 DML 那样对劳动力进行微观管理。联邦学习的最大优势之一是它提供了额外的隐私和安全保证。联邦学习架构依赖于智能设备,例如智能手机和物联网传感器,它们收集和处理自己的数据,因此敏感信息永远不必离开客户端设备。相反,客户端在本地训练子模型并将加密更新发送到中央服务器以聚合到全局模型中。在数据泄露和信息盗窃是常见且严重威胁的世界中,这些强大的隐私保证使联邦学习成为一个有吸引力的选择。该调查概述了联邦学习的数据隐私和安全性的前景和最新发展。我们确定了用于提供隐私和安全性的不同机制,例如差分隐私、安全多方计算和安全聚合。我们还调查了当前的攻击模型,确定了漏洞区域以及攻击者用来渗透联邦系统的策略。调查最后讨论了在这种日益流行的学习范式中未来工作的开放挑战和潜在方向。该调查概述了联邦学习的数据隐私和安全性的前景和最新发展。我们确定了用于提供隐私和安全性的不同机制,例如差分隐私、安全多方计算和安全聚合。我们还调查了当前的攻击模型,确定了漏洞区域以及攻击者用来渗透联邦系统的策略。调查最后讨论了在这种日益流行的学习范式中未来工作的开放挑战和潜在方向。该调查概述了联邦学习的数据隐私和安全性的前景和最新发展。我们确定了用于提供隐私和安全性的不同机制,例如差分隐私、安全多方计算和安全聚合。我们还调查了当前的攻击模型,确定了漏洞区域以及攻击者用来渗透联邦系统的策略。调查最后讨论了在这种日益流行的学习范式中未来工作的开放挑战和潜在方向。识别易受攻击的区域和攻击者用来渗透联邦系统的策略。调查最后讨论了在这种日益流行的学习范式中未来工作的开放挑战和潜在方向。识别易受攻击的区域和攻击者用来渗透联邦系统的策略。调查最后讨论了在这种日益流行的学习范式中未来工作的开放挑战和潜在方向。
更新日期:2020-09-23
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