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。