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.

View More Papers

CP-IoT: A Cross-Platform Monitoring System for Smart Home

Hai Lin (Tsinghua University), Chenglong Li (Tsinghua University), Jiahai Yang (Tsinghua University), Zhiliang Wang (Tsinghua University), Linna Fan (National University of Defense Technology), Chenxin Duan (Tsinghua University)

Read More

GNNIC: Finding Long-Lost Sibling Functions with Abstract Similarity

Qiushi Wu (University of Minnesota), Zhongshu Gu (IBM Research), Hani Jamjoom (IBM Research), Kangjie Lu (University of Minnesota)

Read More

Maginot Line: Assessing a New Cross-app Threat to PII-as-Factor...

Fannv He (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, China), Yan Jia (DISSec, College of Cyber Science, Nankai University, China), Jiayu Zhao (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, China), Yue Fang (National Computer Network Intrusion Protection Center, University of Chinese Academy of Sciences, China),…

Read More

Secure Multiparty Computation of Threshold Signatures Made More Efficient

Harry W. H. Wong (The Chinese University of Hong Kong), Jack P. K. Ma (The Chinese University of Hong Kong), Sherman S. M. Chow (The Chinese University of Hong Kong)

Read More