  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.
