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.

翻译：联邦学习（FL）已成为一种有前景的协作机器学习方法，能够解决数据隐私问题。然而，现有的FL平台和框架在软件工程师的使用中常面临复杂性、有限的自定义选项以及可扩展性限制等挑战。本文介绍了EdgeFL——一种纯边缘侧的轻量级去中心化FL框架，旨在克服FL部署中集中式聚合与可扩展性方面的局限性。通过采用纯边缘侧的模型训练与聚合方法，EdgeFL消除了对中央服务器的依赖，从而实现了跨不同用例的无缝扩展。其集成过程简单，仅需四行代码（LOC），软件工程师即可轻松将FL功能集成到其AI产品中。此外，EdgeFL支持自定义聚合函数，使工程师能够根据特定需求灵活调整。实验结果表明，与现有FL平台/框架相比，EdgeFL实现了更优的性能。我们的实验显示，EdgeFL降低了权重更新延迟，加快了模型进化速度，从而提升了边缘设备的效率。同时，与传统集中式FL方法相比，EdgeFL在分类准确率上也有所提高。借助EdgeFL，软件工程师能够在克服现有FL平台/框架所面临挑战的同时，充分利用联邦学习的优势。