A Survey for Federated Learning Evaluations: Goals and Measures

arXiv:2308.11841v2 Announce Type: replace-cross
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 …

arxiv cs.cr cs.dc cs.lg data evaluation federated federated learning goals machine machine learning novel paradigm privacy purpose sensitive sensitive data sharing survey system train