yobo手机官网：Jamming Attacks on Decentralized Federated Learning in Multi-Hop Wireless Networks
A wireless sensor network is deployed to monitor signal transmissions of interest across a large area. Each sensor receives signals under specific channel conditions based on its location and trains an individual deep neural network model for signal classification. To enhance accuracy, the network utilizes decentralized federated learning over a multi-hop wireless network, allowing collective training of a deep neural network for signal identification. In this approach, sensors broadcast their trained models to neighboring sensors, gather models from neighbors, and aggregate them to initialize their own models for the next round of training. This iterative process builds a common deep neural network across the network while preserving the privacy of signals collected at different locations. Evaluations are conducted to assess signal classification accuracy, convergence time, communication overhead reduction, and energy consumption in various network topologies and packet loss scenarios. The impact of random sensor participation in model updates is also considered. Additionally, we investigate an effective attack strategy that employs jammers to disrupt model exchanges between nodes. Two attack scenarios are examined: First, the adversary can attack any link within a given budget, rendering the two end nodes unable to exchange their models. Second, jammers with limited jamming ranges are deployed, and each jammer can only disrupt nodes within its range. When a directional link is attacked, the receiver node cannot receive the model from the transmitter node. We develop algorithms to select links to be attacked in both scenarios and design algorithms to deploy jammers optimally, maximizing their impact on the decentralized federated learning process. We evaluate these algorithms using wireless signal classification as the use case over a large network area, exploring how these attack mechanisms exploit various aspects of learning, connectivity, and sensing.
石怡，博士，现为美国弗吉利亚理工大学副教授，曾任美国智能自动化公司首席研究员。石怡副教授是国际知名的人工智能安全和优化领域专家，在国际著名期刊和会议上发表论文180多篇，其中单篇文章他引数超过100次的有20多篇，单篇他引次最高的超过800。石怡副教授曾两获无线网络著名会议INFOCOM的最佳论文奖，分别是2008年和2011年，并在2023年获得IEEE INFOCOM Test of Time Award奖。石怡副教授还获得过ACM WUWNet 2014年最佳学生论文奖和IEEE HST 2018年最佳论文奖。石怡副教授担任过多个IEEE和ACM Symposium、Track、Workshop的技术委员会主席，以及IEEE Communications Surveys and Tutorials和IEEE Transactions on Cognitive Communications and Networking的编辑。