BRNES: Enabling Security and Privacy-aware Experience Sharing in Multiagent Robotic and Autonomous Systems
Md Tamjid Hossain, Hung Manh La, Shahriar Badsha, and Anton Netchaev

TL;DR
This paper introduces BRNES, a framework that enhances security and privacy in multiagent reinforcement learning by mitigating adversarial attacks and protecting private information during experience sharing.
Contribution
BRNES is a novel MARL framework that heuristically selects neighbor zones, uses weighted experience aggregation, and applies local differential privacy to secure and improve experience sharing.
Findings
Outperforms state-of-the-art in goal achievement metrics.
Achieves 8.32x faster learning than non-private frameworks.
Provides privacy protection against inference attacks.
Abstract
Although experience sharing (ES) accelerates multiagent reinforcement learning (MARL) in an advisor-advisee framework, attempts to apply ES to decentralized multiagent systems have so far relied on trusted environments and overlooked the possibility of adversarial manipulation and inference. Nevertheless, in a real-world setting, some Byzantine attackers, disguised as advisors, may provide false advice to the advisee and catastrophically degrade the overall learning performance. Also, an inference attacker, disguised as an advisee, may conduct several queries to infer the advisors' private information and make the entire ES process questionable in terms of privacy leakage. To address and tackle these issues, we propose a novel MARL framework (BRNES) that heuristically selects a dynamic neighbor zone for each advisee at each learning step and adopts a weighted experience aggregation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Adversarial Robustness in Machine Learning · Smart Grid Security and Resilience
