Tram-FL: Routing-based Model Training for Decentralized Federated
  Learning
Kota Maejima Takayuki Nishio Asato Yamazaki Yuko Hara-Azumi
In decentralized federated learning (DFL), substantial traffic from frequent
inter-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 transferring
it sequentially amongst nodes, rather than by exchanging and aggregating local
models. We also introduce a dynamic model routing algorithm for optimal route
selection, aimed at enhancing model precision with minimal forwarding. Our
experiments using MNIST, CIFAR-10, and IMDb datasets demonstrate that Tram-FL
with the proposed routing delivers high model accuracy under non-IID
conditions, outperforming baselines while reducing communication costs.