  Federated adversarial training can effectively complement adversarial
robustness into the privacy-preserving federated learning systems. However, the
high demand for memory capacity and computing power makes large-scale federated
adversarial training infeasible on resource-constrained edge devices. Few
previous studies in federated adversarial training have tried to tackle both
memory and computational constraints simultaneously. In this paper, we propose
a new framework named Federated Adversarial Decoupled Learning (FADE) to enable
AT on heterogeneous resource-constrained edge devices. FADE differentially
decouples the entire model into small modules to fit into the resource budget
of each device, and each device only needs to perform AT on a single module in
each communication round. We also propose an auxiliary weight decay to
alleviate objective inconsistency and achieve better accuracy-robustness
balance in FADE. FADE offers theoretical guarantees for convergence and
adversarial robustness, and our experimental results show that FADE can
significantly reduce the consumption of memory and computing power while
maintaining accuracy and robustness.

Machine Learning