FedBat: a self-adapting bat algorithm-based federated learning approach

Federated learning (FL) is an advanced distributed machine learning (ML) framework designed to address issues related to data silos and data privacy. In real-world applications, common problems like non-convex optimization and nonindependent and identically distributed (Non-IID) client data reduce training efficiency, cause local optima, and degrade performance. Therefore, we propose a FL scheme based on the bat algorithm (FedBat), which leverages the echolocation mechanism of bats to effectively balance global and local search capabilities, enabling the algorithm to escape local optima with a certain probability. By combining global optimal model weight optimization with dynamically adjusted search strategies, FedBat guides weaker client models toward the global optimum, thereby accelerating convergence. Additionally, FedBat allows for adaptive parameter adjustments across various datasets. To mitigate client drift, we extend FedBat with Jensen–Shannon (JS) divergence to quantify differences between local and global models. Clients decide whether to upload their local models based on this divergence, to enhance the global model’s generalization and minimize communication overhead. Experimental results demonstrate that FedBat converges 5 times faster and enhances test accuracy by more than 40 \(\%\) $\%$ compared to FedAvg. The extended FedBat effectively mitigates the decrease in the generalization performance of the global model and reduces communication costs by approximately 20 \(\%\) $\%$ . Comparing FedPso, FedGwo, and FedProx shows that FedBat demonstrates superior performance in terms of convergence speed and test accuracy. Furthermore, we derive the formula for the expected convergence rate of FedBat, analyze the impact of various parameters on FL performance, and establish the upper bound of FedBat to evaluate its model divergence.