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

TL;DR

This paper introduces a novel device sampling and data offloading strategy for federated learning that accounts for network heterogeneity and data overlap, improving accuracy and resource efficiency.

Contribution

It develops a joint optimization framework for device sampling and D2D offloading, with a GCN-based learning method for maximizing FedL accuracy under realistic constraints.

Findings

Sampling less than 5% of devices outperforms traditional FedL.

The proposed method improves model accuracy significantly.

Resource utilization is reduced with the new sampling strategy.

Abstract

The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary wireless networks, however: (i) the network may contain heterogeneous communication/computation resources, while (ii) there may be significant overlaps in devices' local data distributions. In this work, we develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading. Our optimization aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy subject to realistic constraints on the network topology and device capabilities. Theoretical analysis of the D2D offloading subproblem leads to new FedL convergence bounds and an efficient sequential convex optimizer. Using this result, we develop a sampling methodology based on graph convolutional networks (GCNs) which learns the relationship between network attributes, sampled nodes, and resulting offloading that maximizes FedL accuracy. Through evaluation on real-world datasets and network measurements from our IoT testbed, we find that our methodology while sampling less than 5% of all devices outperforms conventional FedL substantially both in terms of trained model accuracy and required resource utilization.