SOURCE-LINKED INTELLIGENCE
Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis
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 lo
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T14:53:38.000Z
First collected: 2026-09-20T09:41:04.278Z. This is not the publication date.