報(bào)告題目：Jamming Attacks on Decentralized Federated Learning in Multi-Hop Wireless Networks
報(bào)告人：石怡 副教授
單位：美國弗吉利亞理工大學(xué)
報(bào)告時(shí)間：8月16號（周三）下午14:30-15:30
報(bào)告地點(diǎn)：翡翠科教樓A座1602
報(bào)告摘要：
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.
個(gè)人簡介：
石怡，博士，現(xiàn)為美國弗吉利亞理工大學(xué)副教授，曾任美國智能自動(dòng)化公司首席研究員。石怡副教授是國際知名的人工智能安全和優(yōu)化領(lǐng)域?qū)＜?，在國際著名期刊和會議上發(fā)表論文180多篇，其中單篇文章他引數(shù)超過100次的有20多篇，單篇他引次最高的超過800。石怡副教授曾兩獲無線網(wǎng)絡(luò)著名會議INFOCOM的最佳論文獎(jiǎng)，分別是2008年和2011年，并在2023年獲得IEEE INFOCOM Test of Time Award獎(jiǎng)。石怡副教授還獲得過ACM WUWNet 2014年最佳學(xué)生論文獎(jiǎng)和IEEE HST 2018年最佳論文獎(jiǎng)。石怡副教授擔(dān)任過多個(gè)IEEE和ACM Symposium、Track、Workshop的技術(shù)委員會主席，以及IEEE Communications Surveys and Tutorials和IEEE Transactions on Cognitive Communications and Networking的編輯。
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