Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks
This work addresses performance bottlenecks for deploying AI at the network edge, offering a practical solution for edge computing applications, though it is incremental in nature.
The paper tackles the problem of slow decentralized federated learning on bandwidth-limited edge networks by jointly optimizing communication schemes and mixing matrices, achieving over 80% reduction in total training time without accuracy loss compared to a baseline.
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.