EdgeFL：一种轻量级去中心化联邦学习框架
EdgeFL: A Lightweight Decentralized Federated Learning Framework
AI总结：
EdgeFL是一种仅限边缘的轻量级去中心化联邦学习框架，通过消除中央服务器、简化集成（四行代码）和灵活定制聚合，降低延迟并提升分类准确率，优于现有FL平台。
摘要：
联邦学习（FL）已成为协作机器学习的一种有前景的方法，解决了数据隐私问题。然而，现有的FL平台和框架在复杂性、有限的定制选项和可扩展性限制方面，往往给软件工程师带来挑战。在本文中，我们介绍了EdgeFL，一种仅限边缘的轻量级去中心化FL框架，旨在克服FL部署中集中式聚合和可扩展性的限制。通过采用仅限边缘的模型训练和聚合方法，EdgeFL消除了对中央服务器的需求，实现了跨多种用例的无缝可扩展性。凭借仅需四行代码（LOC）的简单集成过程，软件工程师可以轻松地将FL功能纳入其AI产品中。此外，EdgeFL提供了定制聚合函数的灵活性，使工程师能够根据特定需求进行调整。基于结果，我们证明EdgeFL相比现有FL平台/框架实现了更优越的性能。我们的结果表明，EdgeFL减少了权重更新延迟，实现了更快的模型演化，提高了边缘设备的效率。此外，与传统的集中式FL方法相比，EdgeFL表现出更高的分类准确率。通过利用EdgeFL，软件工程师可以利用联邦学习的优势，同时克服与现有FL平台/框架相关的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
