Network-Aware Optimization of Distributed Learning for Fog Computing

by   Yuwei Tu, et al.

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.


page 1

page 7

page 9

page 10


From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks

Contemporary network architectures are pushing computing tasks from the ...

Infinity: A Scalable Infrastructure for In-Network Applications

Network programmability is an area of research both defined by its poten...

FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems

Federated learning (FL) is capable of performing large distributed machi...

Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation

The conventional federated learning (FedL) architecture distributes mach...

Multi-Stage Hybrid Federated Learning over Large-Scale Wireless Fog Networks

One of the popular methods for distributed machine learning (ML) is fede...

Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing

In this paper, we study a new latency optimization problem for Blockchai...

SOAR: Minimizing Network Utilization with Bounded In-network Computing

In-network computing via smart networking devices is a recent trend for ...

Please sign up or login with your details

Forgot password? Click here to reset