Federated learning (FL) based on the centralized design faces both challenges regarding the trust issue and a single point of failure. To alleviate these issues, blockchain-aided decentralized FL (BDFL) introduces the decentralized network architecture into the FL training process, which can effectively overcome the defects of centralized architecture. However, deploying BDFL in wireless networks usually encounters challenges such as limited bandwidth, computing power, and energy consumption. Driven by these considerations, a dynamic stochastic optimization problem is formulated to minimize the average training delay by jointly optimizing the resource allocation and client selection under the constraints of limited energy budget and client participation. We solve the long-term mixed integer non-linear programming problem by employing the tool of Lyapunov optimization and thereby propose the dynamic resource allocation and client scheduling BDFL (DRC-BDFL) algorithm. Furthermore, we analyze the learning performance of DRC-BDFL and derive an upper bound for convergence regarding the global loss function. Extensive experiments conducted on SVHN and CIFAR-10 datasets demonstrate that DRC-BDFL achieves comparable accuracy to baseline algorithms while significantly reducing the training delay by 9.24% and 12.47%, respectively.

翻译：基于中心化设计的联邦学习（FL）面临着信任问题和单点故障的挑战。为缓解这些问题，基于区块链的去中心化联邦学习（BDFL）将去中心化网络架构引入FL训练过程，可有效克服中心化架构的缺陷。然而，在无线网络中部署BDFL通常会遇到带宽有限、计算能力受限和能耗等挑战。基于这些考量，我们构建了一个动态随机优化问题，旨在有限能量预算和客户端参与约束下，通过联合优化资源分配与客户端选择来最小化平均训练延迟。我们利用李雅普诺夫优化工具求解该长期混合整数非线性规划问题，从而提出了动态资源分配与客户端调度的BDFL（DRC-BDFL）算法。此外，我们分析了DRC-BDFL的学习性能，并推导出全局损失函数收敛性的上界。在SVHN和CIFAR-10数据集上进行的大量实验表明，DRC-BDFL在达到与基线算法相当精度的同时，训练延迟分别显著降低了9.24%和12.47%。