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 goal are heterogeneity in devices compute
resources and topology constraints on which devices can communicate with each
other. We address these challenges by developing the first network-aware
distributed learning optimization methodology where devices optimally share
local data processing 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
to each other, 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 offloading is approximately linear in the range
of computing costs in the network. Our subsequent experiments on testbed
datasets we collect confirm that our algorithms are able to improve network
resource utilization substantially without sacrificing the accuracy of the
learned model. In these experiments, we also study the effect of network
dynamics, quantifying the impact of nodes entering or exiting the network on
model learning and resource costs.

本文提出了一种网络感知分布式学习优化方法，使设备在成本和效益之间进行数据传输和处理决策，并通过测试数据集证明算法能够显著提高网络资源利用效率，同时不损害模型学习准确性。