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Found 3 papers, 1 papers with code
A First Order Meta Stackelberg Method for Robust Federated Learning
Previous research has shown that federated learning (FL) systems are exposed to an array of security risks.
Learning to Backdoor Federated Learning
In particular, we propose a general reinforcement learning-based backdoor attack framework where the attacker first trains a (non-myopic) attack policy using a simulator built upon its local data and common knowledge on the FL system, which is then applied during actual FL training.
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach
We propose a model-based multi-agent reinforcement learning attack framework against federated learning systems.
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