The paper studies a fundamental federated learning (FL) problem involving multiple clients with heterogeneous constrained resources. Compared with the numerous training parameters, the computing and communication resources of clients are insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heterogeneous resources may result in the straggler problem. To address these issues, we propose Fed-RAA: a Resource-Adaptive Asynchronous Federated learning algorithm. Different from vanilla FL methods, where all parameters are trained by each participating client regardless of resource diversity, Fed-RAA adaptively allocates fragments of the global model to clients based on their computing and communication capabilities. Each client then individually trains its assigned model fragment and asynchronously uploads the updated result. Theoretical analysis confirms the convergence of our approach. Additionally, we design an online greedy-based algorithm for fragment allocation in Fed-RAA, achieving fairness comparable to an offline strategy. We present numerical results on MNIST, CIFAR-10, and CIFAR-100, along with necessary comparisons and ablation studies, demonstrating the advantages of our work. To the best of our knowledge, this paper represents the first resource-adaptive asynchronous method for fragment-based FL with guaranteed theoretical convergence.

本研究提出了一种资源自适应异步联邦学习算法(Fed-RAA)，通过根据计算和通信能力分配全局模型的片段给不同的参与客户端，从而解决异构资源下的联邦学习问题。该方法在理论上证实了收敛性，并设计了一种基于贪心算法的在线分配策略，与离线策略相比具有可比拟的公平性。通过对MNIST、CIFAR-10和CIFAR-100的实验结果以及对比和消融研究，证明了本研究的优势。据我们所知，该论文是首个具备理论收敛保证的资源自适应异步的基于片段的联邦学习方法。

面向资源受限环境的自适应联邦学习方法