EdgeFL: A Lightweight Decentralized Federated Learning Framework

TL;DR

EdgeFL is a lightweight, decentralized federated learning framework that eliminates the need for a central server, offering enhanced scalability, customization, and performance for edge device applications.

Contribution

The paper introduces EdgeFL, a novel edge-only decentralized FL framework that simplifies integration, improves scalability, and enhances performance over existing centralized FL systems.

Findings

Reduces weights update latency significantly

Achieves faster model evolution on edge devices

Improves classification accuracy compared to centralized FL

Abstract

Federated Learning (FL) has emerged as a promising approach for collaborative machine learning, addressing data privacy concerns. However, existing FL platforms and frameworks often present challenges for software engineers in terms of complexity, limited customization options, and scalability limitations. In this paper, we introduce EdgeFL, an edge-only lightweight decentralized FL framework, designed to overcome the limitations of centralized aggregation and scalability in FL deployments. By adopting an edge-only model training and aggregation approach, EdgeFL eliminates the need for a central server, enabling seamless scalability across diverse use cases. With a straightforward integration process requiring just four lines of code (LOC), software engineers can easily incorporate FL functionalities into their AI products. Furthermore, EdgeFL offers the flexibility to customize aggregation functions, empowering engineers to adapt them to specific needs. Based on the results, we demonstrate that EdgeFL achieves superior performance compared to existing FL platforms/frameworks. Our results show that EdgeFL reduces weights update latency and enables faster model evolution, enhancing the efficiency of edge devices. Moreover, EdgeFL exhibits improved classification accuracy compared to traditional centralized FL approaches. By leveraging EdgeFL, software engineers can harness the benefits of federated learning while overcoming the challenges associated with existing FL platforms/frameworks.