Federated Learning (FL) enables collaborative model training without sharing raw data, offering a promising solution for privacy-sensitive applications. However, in real-world deployments, significant disparities in client computational capabilities lead to imbalanced model updates, resulting in slow convergence and degraded model generalization. To address this challenge, this paper proposes a novel federated aggregation optimization method, FedAWR, which features adaptive adjustment of learning rates and weights. Specifically, during the global aggregation phase, our method dynamically adjusts each client’s aggregation weight based on its computational capability and configures an appropriate learning rate to balance training progress. Experiments on multi-classification tasks using the Steel Rail Defect and CIFAR-10 datasets demonstrate that the proposed method exhibits significant advantages over mainstream federated algorithms in both convergence efficiency and model generalization performance, thereby validating its effectiveness and superiority.
Tong et al. (2026) studied this question.