Embracing Multiheterogeneity and Privacy Security Simultaneously: A Dynamic Privacy-Aware Federated Reinforcement...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Distributed Systems

Background:

• Federated Reinforcement Learning (FRL) is crucial for privacy-preserving applications but faces challenges with data heterogeneity and weak privacy guarantees.
• Existing FRL methods struggle to address multiple sources of heterogeneity simultaneously while maintaining robust privacy.
• There is a growing demand for FRL solutions that can balance privacy and utility effectively in diverse, real-world scenarios.

Purpose of the Study:

• To propose DPA-FedRL, a novel dynamic privacy-aware federated reinforcement learning framework.
• To address the dual challenges of multiheterogeneity and privacy preservation in FRL.
• To provide a privacy-utility balanced solution for FRL applications.

Main Methods:

• Introduced the concept of "multiheterogeneity" by embedding environmental heterogeneity into agent state representations.
• Incorporated a differentially private mechanism using Gaussian noise with modified global sensitivity tailored for FRL.
• Developed a dynamic privacy budget allocation strategy based on observed heterogeneity levels.

Main Results:

• DPA-FedRL demonstrates superior performance compared to state-of-the-art methods (PPO-DP-SGD, PAvg, QAvg) in highly heterogeneous environments.
• Novel privacy attack simulations quantitatively validate DPA-FedRL's enhanced protection, offering over 1.359x stronger privacy than baselines.
• Theoretical guarantees for convergence, privacy, and sensitivity were rigorously established for the proposed method.

Conclusions:

• DPA-FedRL effectively mitigates multiheterogeneity and enhances privacy in federated reinforcement learning.
• The dynamic privacy budget allocation achieves a favorable balance between privacy and utility.
• This framework represents a significant advancement for privacy-preserving FRL applications.