Federated learning (FL) constitutes a potent machine learning paradigm extensively applied in edge computing for training models on vast datasets. However, the challenges of data imbalance, edge dynamics, and resource constraints in edge computing pose formidable obstacles to sustaining FL efficiency. In addressing these challenges and enhancing the effectiveness of training across heterogeneous devices in unpredictable communication networks, we introduce an asynchronous federated learning framework called AFL-HCS. Within the AFL-HCS framework, client updates transmitted to the parameter server are aggregated in each epoch based on their arrival sequence at the parameter server. Furthermore, the system incorporates a cloud cache structure to store client-submitted training progress for subsequent rounds of global model updates. This mechanism optimally leverages the local progress of clients, expediting the enhancement of the global model’s performance. Experimental results demonstrate that AFL-HCS has significant advantages over the original federated learning protocol. Specifically, AFL-HCS shortens the duration of federated rounds, accelerates the convergence of the global model, and improves the accuracy of the global model, even in unstable edge environments.

联邦学习（FL）构成了一种强大的机器学习范式，广泛应用于边缘计算，用于在海量数据集上训练模型。然而，边缘计算中的数据不平衡、边缘动态和资源限制等挑战对维持 FL 效率构成了巨大的障碍。为了应对这些挑战并提高在不可预测的通信网络中跨异构设备进行训练的有效性，我们引入了一种名为 AFL-HCS 的异步联合学习框架。在 AFL-HCS 框架内，传输到参数服务器的客户端更新根据其到达参数服务器的顺序在每个时期进行聚合。此外，该系统还采用云缓存结构来存储客户提交的训练进度，以供后续几轮全局模型更新。该机制充分利用了客户的本地进度，加速了全局模型性能的提升。实验结果表明，AFL-HCS 相对于原始联邦学习协议具有显着优势。具体来说，AFL-HCS缩短了联邦回合的持续时间，加速了全局模型的收敛，并提高了全局模型的准确性，即使在不稳定的边缘环境中也是如此。