On the Optimization of Model Aggregation for Federated Learning at the Network Edge

TL;DR

This paper proposes an optimized model aggregation strategy for federated learning at the network edge, using overlay networks and ILP-based routing to reduce congestion and improve training reliability.

Contribution

It introduces a novel overlay network approach with ILP and heuristic algorithms to optimize federated learning model aggregation at the network edge.

Findings

Reduced training round failure rates by up to 15%.

Alleviated cloud link congestion.

Enhanced network resource utilization.

Abstract

The rapid increase in connected devices has signifi- cantly intensified the computational and communication demands on modern telecommunication networks. To address these chal- lenges, integrating advanced Machine Learning (ML) techniques like Federated Learning (FL) with emerging paradigms such as Multi-access Edge Computing (MEC) and Software-Defined Wide Area Networks (SD-WANs) is crucial. This paper intro- duces online resource management strategies specifically designed for FL model aggregation, utilizing intermediate aggregation at edge nodes. Our analysis highlights the benefits of incorporating edge aggregators to reduce network link congestion and maximize the potential of edge computing nodes. However, the risk of network congestion persists. To mitigate this, we propose a novel aggregation approach that deploys an aggregator overlay network. We present an Integer Linear Programming (ILP) model and a heuristic algorithm to optimize the routing within this overlay network. Our solution demonstrates improved adapt- ability to network resource utilization, significantly reducing FL training round failure rates by up to 15% while also alleviating cloud link congestion.