  Artificial intelligence (AI) has been introduced in communication networks
and services to improve efficiency via self-optimization. Cooperative
intelligence (CI), also known as collective intelligence and collaborative
intelligence, is expected to become an integral element in next-generation
networks because it can aggregate the capabilities and intelligence of multiple
devices. However, privacy issues may intimidate, obstruct, and hinder the
deployment of CI in practice because collaboration heavily relies on data and
information sharing. Additional practical constraints in communication (e.g.,
limited bandwidth) further limit the performance of CI. To overcome these
challenges, we propose PP-MARL, an efficient privacy-preserving learning scheme
based on multi-agent reinforcement learning (MARL). We apply and evaluate our
scheme in two communication-related use cases: mobility management in
drone-assisted communication and network control with edge intelligence.
Simulation results reveal that the proposed scheme 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.
