Previous research has shown that federated learning (FL) systems are exposed to an array of security risks. Despite the proposal of several defensive strategies, they tend to be non-adaptive and specific to certain types of attacks, rendering them ineffective against unpredictable or adaptive threats. This work models adversarial federated learning as a Bayesian Stackelberg Markov game (BSMG) to capture the defender's incomplete information of various attack types. We propose meta-Stackelberg learning (meta-SL), a provably efficient meta-learning algorithm, to solve the equilibrium strategy in BSMG, leading to an adaptable FL defense. We demonstrate that meta-SL converges to the first-order $\varepsilon$-equilibrium point in $O(\varepsilon^{-2})$ gradient iterations, with $O(\varepsilon^{-4})$ samples needed per iteration, matching the state of the art. Empirical evidence indicates that our meta-Stackelberg framework performs exceptionally well against potent model poisoning and backdoor attacks of an uncertain nature.

本研究提出了一种基于元学习算法的不确定性环境下的联邦学习安全防御机制，该机制将对手建模为Bayesian Stackelberg Markov game，使用meta-SL算法解决均衡策略，从而获得具有适应性的FL防御；实验证明，该元Stackelberg框架在不明确的模型污染和后门攻击中表现出了极好的性能。

鲁棒联邦学习的初阶元斯塔克贝格方法