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Artificial intelligence (AI) has been introduced in communication networksand services to improve efficiency via self-optimization. Cooperativeintelligence (CI), also known as collective intelligence and collaborativeintelligence, is expected to become an integral element in next-generationnetworks because it can aggregate the capabilities and intelligence of multipledevices. However, privacy issues may intimidate, obstruct, and hinder thedeployment of CI in practice because collaboration heavily relies on data andinformation sharing. Additional practical constraints in communication (e.g.,limited bandwidth) further limit the performance of CI. To overcome thesechallenges, we propose PP-MARL, an efficient privacy-preserving learning schemebased on multi-agent reinforcement learning (MARL). We apply and evaluate ourscheme in two communication-related use cases: mobility management indrone-assisted communication and network control with edge intelligence.Simulation results reveal that the proposed scheme can achieve efficient andreliable collaboration with 1.1-6 times better privacy protection and loweroverheads (e.g., 84-91% reduction in bandwidth) than state-of-the-artapproaches.

PP-MARL: Efficient Privacy-Preserving MARL for Cooperative Intelligence in Communication