Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
This work addresses efficiency issues in decentralized federated learning for applications like edge computing, but it is incremental as it extends an existing method (Tram-FL).
The paper tackles the problem of minimizing total training time in decentralized federated learning by proposing Load-aware Tram-FL, which introduces a training scheduling mechanism that accounts for computational and communication loads; simulation results on MNIST and CIFAR-10 show it significantly reduces training time and accelerates convergence compared to baseline methods.
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. To 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 CIFAR-10 demonstrate that Load-aware Tram-FL significantly reduces training time and accelerates convergence compared to baseline methods.