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.

翻译：随着人工智能（AI）赋能的无线通信系统持续演进，分布式学习因其在无线通信应用中能够提供增强的数据隐私保护、改善资源利用率及提升容错能力而受到广泛关注。联邦学习在此基础上进一步增强了跨节点的资源协调能力与模型泛化能力，从而推动实现AI驱动的通信与计算一体化无线网络。本文提出一种新型无线通信系统，以满足对隐私敏感和隐私非敏感用户的个性化服务需求。我们基于多智能体联邦加权深度强化学习（MAFWDRL）设计该系统。该系统在满足用户服务需求的同时，能够实时优化本地通信资源分配，并同步完成计算资源的决策。此外，通过引入探索噪声来增强离线深度强化学习（DRL）在无线信道环境中的探索过程。联邦加权（FedWgt）有效补偿了通信节点间信道状态的异构性差异。大量仿真实验表明，所提方案在吞吐量、计算时延及能耗改善方面显著优于基线方法。