优化边缘网络中的去中心化学习通信，训练速度提升80%以上
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks
- 联合设计代理间通信拓扑与信息融合矩阵
- 实测训练时间减少80%以上，精度不降
- 适合资源受限的多跳边缘网络场景
去中心化联邦学习（DFL）是将人工智能能力引入网络边缘的有前景范式。但在带宽受限的边缘网络上运行DFL时，因代理间频繁参数交换而面临严重性能挑战。现有解决方案基于简化的通信模型，无法刻画多跳带宽受限网络的情况。本文通过联合设计代理构成的叠加网络通信方案与控制代理间通信需求的混合矩阵来解决该问题。通过深入分析问题性质，将每个设计问题转化为可处理的优化问题，并开发出具有性能保障的高效算法。基于真实拓扑和数据的评估表明，所提算法相比基线可将总训练时间降低超过80%，且不牺牲精度，同时在计算效率上显著优于当前最优方法。
原文摘要 · Abstract (English)
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.
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