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Poster
in
Workshop: 2nd ICML Workshop on New Frontiers in Adversarial Machine Learning

A First Order Meta Stackelberg Method for Robust Federated Learning

Yunian Pan · Tao Li · Henger Li · Tianyi Xu · Quanyan Zhu · Zizhan Zheng

Keywords: [ federated learning ] [ Reinforcement Learning ] [ game theory ] [ Robust Machine Learning ]


Abstract: 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.

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