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Safeguarding Privacy and Integrity of Federated Learning in Heterogeneous Cross-Silo IoRT Environments: A Moving Target Defense Approach
IEEE NETWORK ( IF 6.8 ) Pub Date : 2024-02-29 , DOI: 10.1109/mnet.2024.3371535
Zan Zhou 1 , Changqiao Xu 1 , Shujie Yang 1 , Xiaoyan Zhang 1 , Hongjing Li 1 , Sizhe Huang 1 , Gabriel-Miro Muntean 2
Affiliation  

Bridging the gap between the Internet of Things and collaborative robots, the recent advancements in the Internet of Robotic Things (IoRT) aim at significantly improving production and operation efficiency and quality. As the scope and complexity of IoRT continue to expand, involving also very large numbers of robots, there is a need for employment of innovative solutions such as federated learning. However, this growing demand is accompanied by multiple challenges, including threats to data privacy and model integrity. Besides, the heterogeneity of the robots and their interaction, multiplies these challenges. In this paper, we discuss the key concerns of collaborative training in IoRT, and propose a shuffling-based moving target defense approach for federated learning in heterogeneous cross-silo IoRT environments (FedMTD). Based on a hierarchical training structure with node clustering, FedMTD bounds heterogeneity by domains, thereby minimizing the learning error and privacy loss. It also enhances resistance to poisoning attacks through decentralized credit evaluation. Experimental results show that FedMTD brings significant improvements in learning performance, privacy enhancement, and poisoning resistance.

中文翻译:


在异构跨孤岛 IoRT 环境中保护联邦学习的隐私和完整性:移动目标防御方法



机器人物联网(IoRT)的最新进展旨在弥合物联网和协作机器人之间的差距,旨在显着提高生产和运营效率和质量。随着物联网的范围和复杂性不断扩大,还涉及大量机器人,需要采用联邦学习等创新解决方案。然而,这种不断增长的需求伴随着多重挑战,包括对数据隐私和模型完整性的威胁。此外,机器人的异质性及其相互作用使这些挑战成倍增加。在本文中,我们讨论了 IoRT 中协作训练的关键问题,并提出了一种基于洗牌的移动目标防御方法,用于异构跨孤岛 IoRT 环境中的联邦学习(FedMTD)。 FedMTD 基于具有节点聚类的分层训练结构,通过域限制异质性,从而最大限度地减少学习错误和隐私损失。它还通过去中心化的信用评估增强了对中毒攻击的抵抗力。实验结果表明,FedMTD 在学习性能、隐私增强和抗中毒方面带来了显着提升。
更新日期:2024-02-29
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