Federated Learning (FL) is a distributed machine learning paradigm that allows clients to train models on their data while preserving their privacy. FL algorithms, such as Federated Averaging (FedAvg) and its variants, have been shown to converge well in many scenarios. However, these methods require clients to upload their local updates to the server in a synchronous manner, which can be slow and unreliable in realistic FL settings. To address this issue, researchers have developed asynchronous FL methods that allow clients to continue training on their local data using a stale global model. However, most of these methods simply aggregate all of the received updates without considering their relative contributions, which can slow down convergence. In this paper, we propose a contribution-aware asynchronous FL method that takes into account the staleness and statistical heterogeneity of the received updates. Our method dynamically adjusts the contribution of each update based on these factors, which can speed up convergence compared to existing methods.

翻译：联邦学习（FL）是一种分布式机器学习范式，允许客户端在保护数据隐私的前提下利用本地数据训练模型。联邦平均（FedAvg）及其变体等FL算法已被证明在许多场景下具有良好的收敛性。然而，这些方法要求客户端以同步方式向服务器上传本地更新，这在现实FL场景中可能效率低下且不可靠。为解决此问题，研究人员开发了异步FL方法，允许客户端利用过时的全局模型继续在本地数据上进行训练。但现有方法多数直接聚合所有接收到的更新，未考虑其相对贡献，这可能降低收敛速度。本文提出一种考虑贡献度的异步FL方法，该方法能够综合考量接收更新的过时程度与统计异质性，并据此动态调整每个更新的贡献度。与现有方法相比，本方法可有效加速收敛过程。