Federated Learning at the Network Edge: When Not All Nodes are Created Equal

TL;DR

This paper investigates how to improve federated learning at the network edge by considering data quality for node weighting and dropping, revealing that simple methods often underperform and that data quality is crucial.

Contribution

The paper introduces novel insights into node selection strategies in federated learning, emphasizing the importance of data quality over quantity for better performance.

Findings

Simple decision strategies can lead to poor performance.

Considering data quality improves learning outcomes.

Real-world experiments validate the proposed approach.

Abstract

Under the federated learning paradigm, a set of nodes can cooperatively train a machine learning model with the help of a centralized server. Such a server is also tasked with assigning a weight to the information received from each node, and often also to drop too-slow nodes from the learning process. Both decisions have major impact on the resulting learning performance, and can interfere with each other in counterintuitive ways. In this paper, we focus on edge networking scenarios and investigate existing and novel approaches to such model-weighting and node-dropping decisions. Leveraging a set of real-world experiments, we find that popular, straightforward decision-making approaches may yield poor performance, and that considering the quality of data in addition to its quantity can substantially improve learning.