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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems
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Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems

机译：加速联合学习在移动边缘计算系统中的可靠性 - 不可行的客户端

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Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes, and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model's quality in such circumstances. In this article, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge level and the cloud level) of model aggregation enacting different aggregation strategies. Moreover, in order to mitigate stragglers and end device drop-out, we introduce regional slack factors into the stage of client selection performed at the edge nodes using a probabilistic approach without identifying or probing the state of end devices (whose reliability is agnostic). We demonstrate the effectiveness of our method in modulating the proportion of clients selected and present the convergence analysis for our protocol. We have conducted extensive experiments with machine learning tasks in different scales of MEC system. The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model's convergence (by up to 12x) and reducing end device energy consumption (by up to 58 percent).
机译：包含云，边缘节点和终端设备的移动边缘计算（MEC）在将数据处理较近到数据源的数据处理方面表现出很大的潜力。与此同时，联合学习（FL）已成为促进AI应用的有前途的隐私保存方法。但是，在与MEC架构集成时，优化FL的效率和有效性仍然是一个很大的挑战。此外，终端设备的不可靠性（例如，陷阱和间歇丢弃）显着降低了流化过程，并在这种情况下影响全球模型的质量。在本文中，为MEC架构设计了一种名为Hybridfl的多层联合学习协议。 Hybridfl采用模型聚合的两个级别（边缘级别和云级别）颁布了不同的聚合策略。此外，为了减轻陷阱和终端设备辍学，我们将区域松弛因子引入了使用概率方法在边缘节点在边缘节点执行的客户选择阶段，而不识别或探测最终设备的状态（其可靠性是不可知的）。我们展示了我们在调制所选客户比例方面的方法的有效性，并提出了我们协议的收敛分析。我们对MEC系统不同尺度的机器学习任务进行了广泛的实验。结果表明，在缩短联邦圆形长度方面，加速全球模型的收敛性（高达12倍）并降低最终设备能耗（高达58％），加速全球模型的培训流程。

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