Robust Smart Home Face Recognition under Starving Federated Data
This addresses security vulnerabilities in smart home systems, but it appears incremental as it builds on existing adversarial attack and federated learning research.
The paper tackles the problem of adversarial attacks in federated learning for smart home face recognition by introducing a novel federated adversarial training method called FLATS, which makes the global model robust in a starving federated environment.
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.