联邦学习框架的可扩展性与性能评估：一项比较分析
Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis
- University of Sharjah（沙迦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文实验比较FedML、Flower、Substra和OpenFL四个联邦学习框架，发现OpenFL在可扩展性上最优，性能稳定，为框架选择提供指导。
AI中文摘要：
本文对主流的联邦学习（FL）框架FedML、Flower、Substra和OpenFL进行了系统性的考察与实验比较。通过在不同数量的客户端上实现联邦学习，对这些框架进行了实验评估，重点分析了可扩展性和关键性能指标。研究评估了客户端数量增加对总训练时间、损失值和准确率值以及CPU和内存使用情况的影响。结果表明，各框架表现出不同的性能特征：Flower显示出异常高的损失，FedML的准确率范围显著较低，为66%至79%，而Substra在资源效率方面表现良好，尽管其总训练时间呈指数增长。值得注意的是，OpenFL成为最具可扩展性的平台，在不同客户端数量下均展现出一致的准确率、损失和训练时间。OpenFL稳定的CPU和内存使用情况凸显了其在现实场景中的可靠性。这项全面的分析为FL框架的相对性能提供了宝贵的见解，有助于深入理解其能力，并为在不同用户群体中的有效部署提供指导。
英文摘要：
This paper presents a systematic examination and experimental comparison of the prominent Federated Learning (FL) frameworks FedML, Flower, Substra, and OpenFL. The frameworks are evaluated experimentally by implementing Federated Learning over a varying number of clients, emphasizing a thorough analysis of scalability and key performance metrics. The study assesses the impact of increasing client counts on total training time, loss and accuracy values, and CPU and RAM usage. Results indicate distinct performance characteristics among the frameworks, with Flower displaying an unusually high loss, FedML achieving a notably low accuracy range of 66% to 79%, and Substra demonstrating good resource efficiency, albeit with an exponential growth in total training time. Notably, OpenFL emerges as the most scalable platform, demonstrating consistent accuracy, loss, and training time across different client counts. OpenFL's stable CPU and RAM underscore its reliability in real-world scenarios. This comprehensive analysis provides valuable insights into the relative performance of FL frameworks, offering good understanding of their capabilities and providing guidance for their effective deployment across diverse user bases.
