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.

EdgeFL 是一种去中心化、轻量化的边缘 FL 框架，通过采用边缘模型训练和聚合方法来克服集中式聚合和 FL 部署可扩展性的限制，提供了更好的性能、更新延迟和分类准确性。