Efficiently Distributed Federated Learning
Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly. Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance. This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL). FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability. FFL is tested against Intel OpenFL, achieving consistent speedups over diff...
| Authors | Gianluca Mittone, Robert Birke, Marco Aldinucci |
|---|---|
| Primary category | cs.PF |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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