Robust Smart Home Face Recognition under Starving Federated Data
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Abstract (arXiv)
Over the past few years, the field of adversarial attack received numerous attention from various researchers with the help of successful attack success rate against well-known deep neural networks that were acknowledged to achieve high classification ability in various tasks. However, majority of the experiments were completed under a single model, which we believe it may not be an ideal case in a real-life situation. In this paper, we introduce a novel federated adversarial training method for smart home face recognition, named FLATS, where we observed some interesting findings that may not be easily noticed in a traditional adversarial attack to federated learning experiments. By applying different variations to the hyperparameters, we have spotted that our method can make the global model to be robust given a starving federated environment. Our code can be found on https://github.com/jcroh0508/FLATS.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 43 references that are in the map. First posted on arXiv 10 Nov 2022.
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References · 32
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20 more
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