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In decentralized federated learning (DFL), substantial traffic from frequentinter-node communication and non-independent and identically distributed(non-IID) data challenges high-accuracy model acquisition. We propose Tram-FL,a novel DFL method, which progressively refines a global model by transferringit sequentially amongst nodes, rather than by exchanging and aggregating localmodels. We also introduce a dynamic model routing algorithm for optimal routeselection, aimed at enhancing model precision with minimal forwarding. Ourexperiments using MNIST, CIFAR-10, and IMDb datasets demonstrate that Tram-FLwith the proposed routing delivers high model accuracy under non-IIDconditions, outperforming baselines while reducing communication costs.

Tram-FL: Routing-based Model Training for Decentralized Federated Learning