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

arXiv: https://arxiv.org/abs/2506.09769 · HTML: https://mlsanity.com/paper/2506.09769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Do Gradient Inversion Attacks Make Federated Learning Unsafe?](https://mlsanity.com/paper/2202.06924/) (2022)
- [Gossip training for deep learning](https://mlsanity.com/paper/1611.09726/) (2016)
- [How to scale distributed deep learning?](https://mlsanity.com/paper/1611.04581/) (2016)
- [Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space](https://mlsanity.com/paper/2104.00352/) (2021)
- [Model-Based Derivative-Free Optimization Methods and Software](https://mlsanity.com/paper/2210.12018/) (2022)
- [Tram-FL: Routing-based Model Training for Decentralized Federated Learning](https://mlsanity.com/paper/2308.04762/) (2023)

## Built on by (most cited first, 1 of 1)

- [Tram-FL: Reducing Communication and Computation Costs through Sequential Model Circulation in Decentralized Federated Learning](https://mlsanity.com/paper/2610.07859/) (2026)

## Abstract (arXiv)

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.