To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices
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DOI：
10.1109/globecom46510.2021.9685793
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Pavana Prakash;Jiahao Ding;Maoqiang Wu;M. Shu;Rong Yu;M. Pan
中科院分区：
文献类型：
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作者：
Pavana Prakash;Jiahao Ding;Maoqiang Wu;M. Shu;Rong Yu;M. Pan
联邦学习（FL）是一种新兴的分布式机器学习范式，与边缘计算相融合，在移动的边缘设备上具有新的应用前景。在FL中，由于移动的设备在中央服务器的协调下，通过仅共享模型更新，基于自己的数据协作训练模型，因此训练数据保持私有。然而，如果没有数据的集中可用性，计算节点需要经常传达模型更新以实现收敛。因此，创建本地模型更新的本地计算时间沿着将它们发送到服务器和从服务器发送所花费的时间导致总时间的延迟。此外，不可靠的网络连接可能阻碍这些更新的有效通信。为了解决这些问题，在本文中，我们提出了一种延迟高效的FL机制，减少了模型收敛所需的总体时间（包括计算和通信延迟）和通信轮次。探索各种参数对延迟的影响，我们寻求平衡无线通信（通话）和本地计算（工作）之间的权衡。我们制定了一个整体的时间作为一个优化问题的关系，并通过广泛的模拟证明我们的方法的有效性。
Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices. In FL, since mobile devices collaborate to train a model based on their own data under the coordination of a central server by sharing just the model updates, training data is maintained private. However, without the central availability of data, computing nodes need to communicate the model updates often to attain convergence. Hence, the local computation time to create local model updates along with the time taken for transmitting them to and from the server result in a delay in the overall time. Furthermore, unreliable network connections may obstruct an efficient communication of these updates. To address these, in this paper, we propose a delay-efficient FL mechanism that reduces the overall time (consisting of both the computation and communication latencies) and communication rounds required for the model to converge. Exploring the impact of various parameters contributing to delay, we seek to balance the trade-off between wireless communication (to talk) and local computation (to work). We formulate a relation with overall time as an optimization problem and demonstrate the efficacy of our approach through extensive simulations.