面向异构客户端的异步半分散式联邦边缘学习
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients
摘要：
联邦边缘学习（FEEL）作为移动边缘网络的隐私保护分布式学习框架受到了广泛关注。在这项工作中，我们研究了一种新型的半分散FEEL（SD-FEEL）架构，其中多个边缘服务器协作以在训练中整合来自边缘设备的更多数据。尽管通过快速边缘聚合实现了低训练延迟，但计算资源中的设备异构性使效率恶化。本文提出了一种SD-FEEL的异步训练算法来克服这个问题，其中边缘服务器可以独立地为关联的客户端节点设置最后期限并触发模型聚合。针对不同程度的失效，设计了一种失效感知聚合方案，并分析了其收敛性能。仿真结果表明，我们提出的算法在实现更快的收敛速度和更好的学习性能的有效性。
英文摘要：
Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-decentralized FEEL (SD-FEEL) architecture where multiple edge servers collaborate to incorporate more data from edge devices in training. Despite the low training latency enabled by fast edge aggregation, the device heterogeneity in computational resources deteriorates the efficiency. This paper proposes an asynchronous training algorithm for SD-FEEL to overcome this issue, where edge servers can independently set deadlines for the associated client nodes and trigger the model aggregation. To deal with different levels of staleness, we design a staleness-aware aggregation scheme and analyze its convergence performance. Simulation results demonstrate the effectiveness of our proposed algorithm in achieving faster convergence and better learning performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
