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

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.

@article{kainuma2025_2506.09769,
  title={ Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning },
  author={ Haruki Kainuma and Takayuki Nishio },
  journal={arXiv preprint arXiv:2506.09769},
  year={ 2025 }
}