  As artificial intelligence (AI)-enabled wireless communication systems
continue their evolution, distributed learning has gained widespread attention
for its ability to offer enhanced data privacy protection, improved resource
utilization, and enhanced fault tolerance within wireless communication
applications. Federated learning further enhances the ability of resource
coordination and model generalization across nodes based on the above
foundation, enabling the realization of an AI-driven communication and
computing integrated wireless network. This paper proposes a novel wireless
communication system to cater to a personalized service needs of both
privacy-sensitive and privacy-insensitive users. We design the system based on
based on multi-agent federated weighting deep reinforcement learning (MAFWDRL).
The system, while fulfilling service requirements for users, facilitates
real-time optimization of local communication resources allocation and
concurrent decision-making concerning computing resources. Additionally,
exploration noise is incorporated to enhance the exploration process of
off-policy deep reinforcement learning (DRL) for wireless channels. Federated
weighting (FedWgt) effectively compensates for heterogeneous differences in
channel status between communication nodes. Extensive simulation experiments
demonstrate that the proposed scheme outperforms baseline methods significantly
in terms of throughput, calculation latency, and energy consumption
improvement.
