Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning

E-mail: nishio@ict.eng.isct.ac.jp Abstract --This paper proposes Load-aware Tram-FL, an extension of Tram-FL that introduces a training scheduling mechanism to minimize total training time in decentralized federated learning by accounting for both computational and communication loads. The scheduling problem is formulated as a global optimization task, which--though intractable in its original form--is made solvable by decomposing it into node-wise subproblems. T o promote balanced data utilization under non-IID distributions, a variance constraint is introduced, while the overall training latency, including both computation and communication costs, is minimized through the objective function. Simulation results on MNIST and CIF AR-10 demonstrate that Load-aware Tram-FL significantly reduces training time and accelerates convergence compared to baseline methods. Federated learning (FL) enables model training without exporting data, making it particularly effective for privacy-sensitive applications.