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PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications

Tingting Yuan, Hwei-Ming Chung, Xiaoming Fu

TL;DR

PP-MARL addresses privacy and overhead barriers in MARL-based cooperative intelligence for communications by combining decentralized execution with centralized training, hierarchical critics, and memory architectures. It encodes raw data and protects shared information with $HE$ and $DP$, while split-learning reduces communication load. The approach is evaluated in drone mobility management and edge-assisted network control, demonstrating improved privacy protection and substantial bandwidth reductions compared with baselines. This work enables practical deployment of CI in next-generation networks with robust privacy-preserving multi-agent learning.

Abstract

Cooperative intelligence (CI) is expected to become an integral element in next-generation networks because it can aggregate the capabilities and intelligence of multiple devices. Multi-agent reinforcement learning (MARL) is a popular approach for achieving CI in communication problems by enabling effective collaboration among agents to address sequential problems. However, ensuring privacy protection for MARL is a challenging task because of the presence of heterogeneous agents that learn interdependently via sharing information. Implementing privacy protection techniques such as data encryption and federated learning to MARL introduces the notable overheads (e.g., computation and bandwidth). To overcome these challenges, we propose PP-MARL, an efficient privacy-preserving learning scheme for MARL. PP-MARL leverages homomorphic encryption (HE) and differential privacy (DP) to protect privacy, while introducing split learning to decrease overheads via reducing the volume of shared messages, and then improve efficiency. We apply and evaluate PP-MARL in two communication-related use cases. Simulation results reveal that PP-MARL can achieve efficient and reliable collaboration with 1.1-6 times better privacy protection and lower overheads (e.g., 84-91% reduction in bandwidth) than state-of-the-art approaches.

PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications

TL;DR

PP-MARL addresses privacy and overhead barriers in MARL-based cooperative intelligence for communications by combining decentralized execution with centralized training, hierarchical critics, and memory architectures. It encodes raw data and protects shared information with and , while split-learning reduces communication load. The approach is evaluated in drone mobility management and edge-assisted network control, demonstrating improved privacy protection and substantial bandwidth reductions compared with baselines. This work enables practical deployment of CI in next-generation networks with robust privacy-preserving multi-agent learning.

Abstract

Cooperative intelligence (CI) is expected to become an integral element in next-generation networks because it can aggregate the capabilities and intelligence of multiple devices. Multi-agent reinforcement learning (MARL) is a popular approach for achieving CI in communication problems by enabling effective collaboration among agents to address sequential problems. However, ensuring privacy protection for MARL is a challenging task because of the presence of heterogeneous agents that learn interdependently via sharing information. Implementing privacy protection techniques such as data encryption and federated learning to MARL introduces the notable overheads (e.g., computation and bandwidth). To overcome these challenges, we propose PP-MARL, an efficient privacy-preserving learning scheme for MARL. PP-MARL leverages homomorphic encryption (HE) and differential privacy (DP) to protect privacy, while introducing split learning to decrease overheads via reducing the volume of shared messages, and then improve efficiency. We apply and evaluate PP-MARL in two communication-related use cases. Simulation results reveal that PP-MARL can achieve efficient and reliable collaboration with 1.1-6 times better privacy protection and lower overheads (e.g., 84-91% reduction in bandwidth) than state-of-the-art approaches.
Paper Structure (20 sections, 5 figures, 1 table)

This paper contains 20 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. MARL for CI in Communications
  3. MARL for CI
  4. Use Case Studies
  5. Mobility management in drone-assisted communication
  6. Network control with edge intelligence
  7. Existing Privacy-Preserving Schemes for MARL
  8. Federated learning (FL)
  9. Data encoding
  10. Data encryption
  11. Key Design of PP-MARL
  12. Execution and Training in PP-MARL
  13. Privacy Protection in PP-MARL
  14. Raw data encoding
  15. Preventing inference and differential attacks
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Cooperative intelligence for communications with case studies.
  • Figure 2: Agent cooperation modes.
  • Figure 3: PP-MARL: distributed execution and centralized learning with hierarchical critics and memories. HE is used to offer protection over q values; DP prevents the inference of r from the loss value. o, a, r, q, Q stand for observations, actions, rewards, q values and Q values.
  • Figure 4: Drone-assisted communication: mean number of target places covered by drones (per episode) in different observation ranges.
  • Figure 5: Network control with edge intelligence: mean delay over time. PP-MARL can reduce delay than the others, which is more obvious during rush hours.