Computer Science > Machine Learning
arXiv:2308.11841 (cs)
[Submitted on 23 Aug 2023 (v1), last revised 23 Mar 2024 (this version, v2)]
Title:A Survey for Federated Learning Evaluations: Goals and Measures
View a PDF of the paper titled A Survey for Federated Learning Evaluations: Goals and Measures, by Di Chai and 5 other authors
View PDFHTML (experimental)Abstract:Evaluation is a systematic approach to assessing how well a system achieves its intended purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine learning that allows multiple parties to collaboratively train models without sharing sensitive data. However, evaluating FL is challenging due to its interdisciplinary nature and diverse goals, such as utility, efficiency, and security. In this survey, we first review the major evaluation goals adopted in the existing studies and then explore the evaluation metrics used for each goal. We also introduce FedEval, an open-source platform that provides a standardized and comprehensive evaluation framework for FL algorithms in terms of their utility, efficiency, and security. Finally, we discuss several challenges and future research directions for FL evaluation.
| Subjects: | Machine Learning (cs.LG); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC) |
| Cite as: | arXiv:2308.11841 [cs.LG] |
| (orarXiv:2308.11841v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2308.11841 arXiv-issued DOI via DataCite |
Submission history
From: Di Chai [view email][v1] Wed, 23 Aug 2023 00:17:51 UTC (14,175 KB)
[v2] Sat, 23 Mar 2024 08:45:03 UTC (7,676 KB)
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View a PDF of the paper titled A Survey for Federated Learning Evaluations: Goals and Measures, by Di Chai and 5 other authors
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