When to Intervene? State-Aware Sparse Manipulation in Federated Reinforcement Learning
Federated reinforcement learning (FRL) enables distributed agents to collaboratively train decision-making policies, but its decentralized training process also exposes global policy learning to Byzantine manipulation. Existing poisoning attacks primarily focus on how to construct malicious updates, while trajectory-level intervention timing remains largely implicit. In sequential decision making, however, where an intervention is applied can alter subsequent trajectories and learning signals. Through controlled experiments, we find that changing the selected trajectory states materially alters attack efficacy even when the malicious-update construction is fixed. We therefore identify whe...
| Authors | Shutong Zheng, Sijia Chen |
|---|---|
| Primary category | cs.LG |
| Published | 8 October 2026 |
| Last updated at source | 9 October 2026 |
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