ML
ICCV

Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology

July 31, 2023

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

Overall

< 1 minute

Tuong Do, Binh Nguyen, Vuong Pham, Toan Tran, Erman Tjiputra, Quang Tran, Anh Nguyen

ICCV 2023

Share Article

Related publications

ML
ICML Top Tier
May 16, 2024

Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung

ML
ICML Top Tier
May 16, 2024

Vy Vo, Trung Le, Tung-Long Vuong, He Zhao, Edwin V. Bonilla, Dinh Phung

ML
ICML Top Tier
May 14, 2024

Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying

GenAI
ML
ICML Top Tier
May 14, 2024

Bao Nguyen, Binh Nguyen, Hieu Nguyen, Viet Anh Nguyen