6163am银河线路工业工程与管理系学术报告

题目：Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things

报告人: Andi WANG, Asst Professor, School of Manufacturing Systems and Networks, Arizona State University

地点：6163am银河线路王克桢楼1003会议室

时间：8月3日(周四)10:00-11:30

主持人：张玺  副教授

Abstract: The Internet of Federated Things (IoFT) represents a network of interconnected systems with federated learning as the backbone, facilitating collaborative knowledge acquisition while ensuring data privacy for individual systems. The wide adoption of IoFT, however, is hindered by security concerns, particularly the susceptibility of federated learning networks to adversarial attacks. In this paper, we propose an effective non-parametric approach FedRR, which leverages the low-rank features of the transmitted parameter updates generated by federated learning to address the adversarial attack problem. Besides, our proposed method is capable of accurately detecting adversarial clients and controlling the false alarm rate under the scenario with no attack occurring. Experiments based on digit recognition using the MNIST datasets validated the advantages of our approach.

Short Bio: Dr. Andi Wang obtained his PhD from Georgia Institute of Technology and is currently an assistant professor in Arizona State University. His research focuses on the intersection of data science and manufacturing systems. His research involves data-driven root-cause diagnostics, monitoring, design optimization, prediction for complex, interconnected, and intelligent systems.

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