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Previous research has shown that federated learning (FL) systems are exposedto an array of security risks. Despite the proposal of several defensivestrategies, they tend to be non-adaptive and specific to certain types ofattacks, rendering them ineffective against unpredictable or adaptive threats.This work models adversarial federated learning as a Bayesian StackelbergMarkov game (BSMG) to capture the defender's incomplete information of variousattack types. We propose meta-Stackelberg learning (meta-SL), a provablyefficient meta-learning algorithm, to solve the equilibrium strategy in BSMG,leading to an adaptable FL defense. We demonstrate that meta-SL converges tothe 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 ourmeta-Stackelberg framework performs exceptionally well against potent modelpoisoning and backdoor attacks of an uncertain nature.

A First Order Meta Stackelberg Method for Robust Federated Learning