Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology
This work addresses the challenge of slow training times in federated learning for applications involving reliable data silos, representing an incremental improvement over existing topology designs.
The paper tackles the problem of reducing training time in cross-silo federated learning by proposing a new multigraph topology that allows model aggregation without waiting for all nodes, achieving significant reductions in training time compared to state-of-the-art methods while maintaining model accuracy.
Federated learning is an active research topic since it enables several participants to jointly train a model without sharing local data. Currently, cross-silo federated learning is a popular training setting that utilizes a few hundred reliable data silos with high-speed access links to training a model. While this approach has been widely applied in real-world scenarios, designing a robust topology to reduce the training time remains an open problem. In this paper, we present a new multigraph topology for cross-silo federated learning. We first construct the multigraph using the overlay graph. We then parse this multigraph into different simple graphs with isolated nodes. The existence of isolated nodes allows us to perform model aggregation without waiting for other nodes, hence effectively reducing the training time. Intensive experiments on three public datasets show that our proposed method significantly reduces the training time compared with recent state-of-the-art topologies while maintaining the accuracy of the learned model. Our code can be found at https://github.com/aioz-ai/MultigraphFL