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Network-Aware Optimization of Distributed Learning for Fog Computing

机译：雾计算的分布式学习的网络感知优化

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Fog computing promises to enable machine learning tasks to scale to large amounts of data by distributing processing across connected devices. Two key challenges to achieving this are (i) heterogeneity in devices’ compute resources and (ii) topology constraints on which devices can communicate. We are the first to address these challenges by developing a network-aware distributed learning optimization methodology where devices process data for a task locally and send their learnt parameters to a server for aggregation at certain time intervals. Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points. We analytically characterize the optimal data transfer solution for different fog network topologies, showing for example that the value of a device offloading is approximately linear in the range of computing costs in the network. Our subsequent experiments on both synthetic and real-world datasets we collect confirm that our algorithms are able to improve network resource utilization substantially without sacrificing the accuracy of the learned model.
机译：雾计算有望通过在连接的设备之间分布处理来使机器学习任务扩展到大量数据。实现此目标的两个主要挑战是（i）设备计算资源的异构性和（ii）设备可以通信的拓扑约束。我们是第一个通过开发一种网络感知的分布式学习优化方法来解决这些挑战的方法，该方法可以使设备在本地处理任务的数据，并将其学习到的参数发送到服务器以在一定的时间间隔进行聚合。与传统的联合学习框架不同，我们的方法使设备能够卸载其数据处理任务，这些决定是通过凸数据传输优化问题确定的，该问题可以权衡与设备处理，卸载和丢弃数据点相关的成本。我们分析性地描述了针对不同雾网络拓扑的最佳数据传输解决方案，例如，在网络中计算成本的范围内，设备卸载的价值大约是线性的。我们随后对收集的合成数据集和实际数据集进行的实验证实，我们的算法能够在不牺牲学习模型准确性的前提下，大幅提高网络资源利用率。

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