Abstract
Smartphones have become a part of everyday life, and users are contributing to Machine Learning with a simple touch (ML). Federated Learning (FL) is a new collaborative learning technique that preserves privacy and addresses the problem of traditional ML. Despite this, it has a large attack surface area and is vulnerable to privacy attacks. Studying the impact of such attacks on the resulting FL models is an important research topic. Currently, there is a lack of an experimental platform to conduct such studies. We attempt to bridge this gap in this paper by proposing the Federated Learning Attack Simulation (FLAS) platform. It is a web-based application designed with an easy-to-use workflow for non-experts and the ability to accelerate testing and analysis for Federated Learning (FL) professionals. Preliminary evaluations have demonstrated the effectiveness of FLAS in supporting the study of common privacy attacks on FL.
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Acknowledgments
This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2020-019); the Joint NTU-WeBank Research Centre on Fintech (Award No: NWJ-2020-008); the Nanyang Assistant Professorship (NAP); and the RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund (No. A20G8b0102), Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.
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Loh, Y., Chen, Z., Zhao, Y., Yu, H. (2022). FLAS: A Platform for Studying Attacks on Federated Learning. In: Meiselwitz, G. (eds) Social Computing and Social Media: Design, User Experience and Impact. HCII 2022. Lecture Notes in Computer Science, vol 13315. Springer, Cham. https://doi.org/10.1007/978-3-031-05061-9_12
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