Back to Search
Start Over
UniFed: A Benchmark for Federated Learning Frameworks
- Publication Year :
- 2022
- Publisher :
- arXiv, 2022.
-
Abstract
- Federated Learning (FL) has become a practical and popular paradigm in machine learning. However, currently, there is no systematic solution that covers diverse use cases. Practitioners often face the challenge of how to select a matching FL framework for their use case. In this work, we present UniFed, the first unified benchmark for standardized evaluation of the existing open-source FL frameworks. With 15 evaluation scenarios, we present both qualitative and quantitative evaluation results of nine existing popular open-sourced FL frameworks, from the perspectives of functionality, usability, and system performance. We also provide suggestions on framework selection based on the benchmark conclusions and point out future improvement directions.<br />Comment: Code: https://github.com/AI-secure/FLBenchmark-toolkit Website: https://unifedbenchmark.github.io/
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....e952861dd5d90171c247d3b3d4a566ec
- Full Text :
- https://doi.org/10.48550/arxiv.2207.10308