A First Order Meta Stackelberg Method for Robust Federated Learning (Technical Report)

TL;DR

This paper introduces a novel meta-Stackelberg approach for federated learning that enhances robustness against uncertain and adaptive security threats by modeling adversaries as a Bayesian Stackelberg game.

Contribution

It proposes a first-order meta-Stackelberg method to improve federated learning security, addressing limitations of existing defenses against adaptive attacks.

Findings

Outperforms existing defenses against model poisoning.

Effective against diverse backdoor attack types.

Enhances adaptability and resilience in federated learning.

Abstract

Recent research efforts indicate that federated learning (FL) systems are vulnerable to a variety of security breaches. While numerous defense strategies have been suggested, they are mainly designed to counter specific attack patterns and lack adaptability, rendering them less effective when facing uncertain or adaptive threats. This work models adversarial FL as a Bayesian Stackelberg Markov game (BSMG) between the defender and the attacker to address the lack of adaptability to uncertain adaptive attacks. We further devise an effective meta-learning technique to solve for the Stackelberg equilibrium, leading to a resilient and adaptable defense. The experiment results suggest that our meta-Stackelberg learning approach excels in combating intense model poisoning and backdoor attacks of indeterminate types.