Fedavg-Db: A Dual-Loop Dynamic Batch Optimization Framework for Efficient and Robust Federated Learning

Federated Learning (FL), as a distributed machine learning paradigm, demonstrates significant potential in privacy-preserving scenarios but faces dual challenges of client data non-Independent and Identically Distributed (non-IID) characteristics and heterogeneous computing resources. Existing methods like FedAvg employ fixed batch sizes, leading to constrained convergence rates and suboptimal resource utilization. This paper proposes FedAvg-DB, a dynamic batch adjustment framework featuring a dual-loop optimization architecture: At the client level, a three-phase sliding window mechanism monitors loss improvement rate (r_k) and variance (σ_k) in real-time, establishing a threshold-triggered adaptive adjustment system; At the server level, an innovative aggregation strategy integrating median calibration and momentum smoothing effectively mitigates data distribution bias. Experiments on CIFAR-10/100 and FEMNIST datasets demonstrate that the proposed method achieves 18.3% faster convergence, 34.6% lower client loss variance, and 10.3% energy reduction compared to traditional FedAvg. Ablation studies confirm the synergistic effects of dynamic batch adjustment and learning rate square-root scaling rules, validating the framework's effectiveness in practical applications such as medical image segmentation (5.2% Dice coefficient improvement) and industrial predictive maintenance (12.7% RMSE reduction).