To enhance straggler resilience in federated learning (FL) systems, a semi-decentralized approach has been recently proposed, enabling collaboration between clients. Unlike the existing semi-decentralized schemes, which adaptively adjust the collaboration weight according to the network topology, this letter proposes a deterministic coded network that leverages wireless diversity for semi-decentralized FL without requiring prior information about the entire network. Furthermore, the theoretical analyses of the outage and the convergence rate of the proposed scheme are provided. Finally, the superiority of our proposed method over benchmark methods is demonstrated through comprehensive simulations.

翻译：为增强联邦学习（FL）系统中的掉队者鲁棒性，近期提出了一种半去中心化方法，使客户端之间能够协作。与现有根据网络拓扑自适应调整协作权重的半去中心化方案不同，本文提出了一种确定性编码网络，该网络利用无线分集实现半去中心化联邦学习，且无需整个网络的先验信息。此外，本文对所提方案的中断概率与收敛速率进行了理论分析。最后，通过综合仿真验证了所提方法相较于基准方法的优越性。