Communication-Efficient ADMM-based Federated Learning

Abstract

Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational costs, and whether they converge. To address these issues, this paper proposes exact and inexact ADMM-based federated learning. They are not only communication-efficient but also converge linearly under very mild conditions, such as convexity-free and irrelevance to data distributions. Moreover, the inexact version has low computational complexity, thereby alleviating the computational burdens significantly.

References in corpus (5)

• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning
• Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
• Federated Nonconvex Sparse Learning
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Cited by in corpus (3)

• Federated PCA on Grassmann Manifold for IoT Anomaly Detection
• Confederated Learning: Federated Learning with Decentralized Edge Servers
• Federated Learning Using Three-Operator ADMM