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arXiv · 2211.05410

Robust Smart Home Face Recognition under Starving Federated Data

Jaechul Roh·Yajun Fang
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Abstract

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.

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Jaechul Roh, Yajun Fang. 2022-11-19. Robust Smart Home Face Recognition under Starving Federated Data. https://arxiv.org/abs/2211.05410

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