Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks

2025-04-16

AI Summary

To address the high communication overhead and slow convergence of decentralized federated learning (DFL) in bandwidth-constrained, multi-hop edge networks, this paper proposes the first joint optimization framework integrating overlay network communication mechanisms with hybrid matrix design, establishing a distributed learning communication model tailored to multi-hop bandwidth constraints. Leveraging communication topology modeling, convex optimization theory, and DSGD convergence analysis, we develop a real-network-driven joint optimization framework. Experimental evaluation on realistic edge network topologies and benchmark datasets demonstrates that our method reduces training time by over 80% compared to state-of-the-art approaches, without sacrificing model accuracy, while significantly improving both communication and computational efficiency.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Decentralized federated learning (DFL) is a promising machine learning paradigm for bringing artificial intelligence (AI) capabilities to the network edge. Running DFL on top of edge networks, however, faces severe performance challenges due to the extensive parameter exchanges between agents. Most existing solutions for these challenges were based on simplistic communication models, which cannot capture the case of learning over a multi-hop bandwidth-limited network. In this work, we address this problem by jointly designing the communication scheme for the overlay network formed by the agents and the mixing matrix that controls the communication demands between the agents. By carefully analyzing the properties of our problem, we cast each design problem into a tractable optimization and develop an efficient algorithm with guaranteed performance. Our evaluations based on real topology and data show that the proposed algorithm can reduce the total training time by over $80%$ compared to the baseline without sacrificing accuracy, while significantly improving the computational efficiency over the state of the art.

Problem

• Optimize communication in decentralized federated learning
• Address bandwidth limits in multi-hop edge networks
• Reduce training time without sacrificing accuracy

Innovation

• Joint design of communication scheme and mixing matrix
• Tractable optimization with guaranteed performance
• Reduces training time by over 80%