As a dominant edge intelligence technique, Federated Learning (FL) can reduce the data transmission volume, shorten the communication latency and improve the collaboration efficiency among end-devices and edge servers. Existing works on FL-based edge computing only take device- and resource-heterogeneity into consideration under a fixed loss-minimization objective. As heterogeneous end-devices are usually assigned with various tasks with different target accuracies, task heterogeneity is also a significant issue and has not yet been investigated. To this end, we propose a Customized FL (CuFL) algorithm with an adaptive learning rate to tailor for heterogeneous accuracy requirements and to accelerate the local training process. We also present a fair global aggregation strategy for the edge server to minimize the variance of accuracy gaps among heterogeneous end-devices. We rigorously analyze the convergence property of the CuFL algorithm in theory. We also verify the feasibility and effectiveness of the CuFL algorithm in the vehicle classification task. Evaluation results demonstrate that our algorithm performs better in terms of the accuracy rate, training time, and fairness during aggregation than existing efforts.

联合学习（FL）作为一种主要的边缘智能技术，可以减少数据传输量，缩短通信延迟并提高最终设备与边缘服务器之间的协作效率。现有的基于FL的边缘计算工作仅在固定的损耗最小化目标下才考虑设备和资源的异构性。由于异构终端设备通常分配有具有不同目标精度的各种任务，因此任务异构性也是一个重要问题，尚未进行研究。为此，我们提出了一种具有自适应学习率的定制FL（CuFL）算法，以适应异构精度要求并加速本地训练过程。我们还为边缘服务器提出了一个公平的全局聚合策略，以最大程度地减少异构终端设备之间的精度差距。我们从理论上严格分析了CuFL算法的收敛性。我们还验证了CuFL算法在车辆分类任务中的可行性和有效性。评估结果表明，在聚合过程中，我们的算法在准确率，训练时间和公平性方面比现有工作表现更好。