A Framework for testing Federated Learning algorithms using an edge-like environment

TL;DR

This paper presents a scalable framework for testing federated learning algorithms in an edge-like environment, addressing challenges in evaluation due to data heterogeneity and system distribution.

Contribution

The authors introduce a novel framework for evaluating federated learning algorithms using a containerized, edge-like environment managed by Kubernetes, simplifying assessment processes.

Findings

Framework enables scalable FL algorithm testing

Evaluation conducted in a realistic edge-like environment

Addresses data heterogeneity challenges in FL

Abstract

Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where the data is being created and where actions are occurring, enabling faster response times, greater data privacy, and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, it is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation. This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. In general, testing and assessing FL algorithms can be a very difficult and complex task due to the distributed nature of the systems. In this work, a framework is proposed and implemented to assess FL algorithms in a more easy and scalable way. This framework is evaluated over a distributed edge-like environment managed by a container orchestration platform (i.e. Kubernetes).