Tianyue Chu, Devriş İşler (IMDEA Networks Institute & Universidad Carlos III de Madrid), Nikolaos Laoutaris (IMDEA Networks Institute)

Federated Learning (FL) has evolved into a pivotal paradigm for collaborative machine learning, enabling a centralised server to compute a global model by aggregating the local models trained by clients. However, the distributed nature of FL renders it susceptible to poisoning attacks that exploit its linear aggregation rule called FEDAVG. To address this vulnerability, FEDQV has been recently introduced as a superior alternative to FEDAVG, specifically designed to mitigate poisoning attacks by taxing more than linearly deviating clients. Nevertheless, FEDQV remains exposed to privacy attacks that aim to infer private information from clients’ local models. To counteract such privacy threats, a well-known approach is to use a Secure Aggregation (SA) protocol to ensure that the server is unable to inspect individual trained models as it aggregates them. In this work, we show how to implement SA on top of FEDQV in order to address both poisoning and privacy attacks. We mount several privacy attacks against FEDQV and demonstrate the effectiveness of SA in countering them.

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Shaofei Li (Key Laboratory of High-Confidence Software Technologies (MOE), School of Computer Science, Peking University), Feng Dong (Huazhong University of Science and Technology), Xusheng Xiao (Arizona State University), Haoyu Wang (Huazhong University of Science and Technology), Fei Shao (Case Western Reserve University), Jiedong Chen (Sangfor Technologies Inc.), Yao Guo (Key Laboratory of High-Confidence Software Technologies…

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FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks...

Hossein Fereidooni (Technical University of Darmstadt), Alessandro Pegoraro (Technical University of Darmstadt), Phillip Rieger (Technical University of Darmstadt), Alexandra Dmitrienko (University of Wuerzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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LARMix: Latency-Aware Routing in Mix Networks

Mahdi Rahimi (KU Leuven), Piyush Kumar Sharma (KU Leuven), Claudia Diaz (KU Leuven)

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SENSE: Enhancing Microarchitectural Awareness for TEEs via Subscription-Based Notification

Fan Sang (Georgia Institute of Technology), Jaehyuk Lee (Georgia Institute of Technology), Xiaokuan Zhang (George Mason University), Meng Xu (University of Waterloo), Scott Constable (Intel), Yuan Xiao (Intel), Michael Steiner (Intel), Mona Vij (Intel), Taesoo Kim (Georgia Institute of Technology)

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