- Chen Chen
- Jie Zhang
- Lingjuan Lyu
GEAR: A Margin-based Federated Adversarial Training Approach
Previous studies have shown that federated learning (FL) is vulnerable to well-crafted adversarial examples. Some recent efforts tried to combine adversarial training with FL, i.e., federated adversarial training (FAT), in order to achieve adversarial robustness in FL. However, most of the existing FAT works suffer from either low natural accuracy or low robust accuracy. Moreover, none of these works provide a more in-depth understanding of the challenges behind adversarial robustness in FL. To address these issues, we propose a novel marGin-based fEderated Adversarial tRaining Approach called GEAR. It encourages the minority classes to have larger margins by introducing a margin-based cross-entropy loss, and regularizes the decision boundary to be smooth by introducing a regularization loss, thus providing a better decision boundary for the global model. To the best of our knowledge, this work is the first to investigate the impact of decision boundary on FAT and delivers the best natural accuracy and robust accuracy in FL by far. Extensive experiments on multiple datasets across various settings all validate the effectiveness of our proposed method. For example, on SVHN dataset, GEAR can improve the natural accuracy and robust accuracy (against FGSM attack) of the best baseline method (FedTRADES) by 20.17\% and 10.73\%, respectively.
MocoSFL: enabling cross-client collaborative self-supervised learning
Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe…
IDEAL: Query-Eﬀicient Data-Free Learning from Black-Box Models
Knowledge Distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model. However, most KD methods require access to either the teacher's training data or model parameter, which is unrealistic. To tackle this prob…
Twofer: Tackling Continual Domain Shift with Simultaneous Domain Generalization and Adaptation
In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptat…
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