Federated Learning Trade-Offs: A Systematic Review of Privacy Protection and Performance Optimization
Abstract
Federated Learning (FL) is an innovative approach in Artificial Intelligence (AI) that enhances privacy by avoiding centralized data storage and performing learning directly on users' devices. However, this method introduces new privacy concerns, especially during the training phase and when exchanging parameters between servers and clients. Although various privacy-preserving solutions have been developed to address these issues, integrating these mechanisms can lead to increased communication and computational overheads. This, in turn, may affect data utility and the performance metrics of learning systems. This paper presents a systematic literature review of key methods and metrics that help strike a balance between privacy and other performance aspects in FL applications, such as accuracy, loss, convergence time, utility, and overheads in communication and computation. The review offers a comprehensive overview of recent privacy-preserving techniques in FL across different applications, with a special emphasis on quantitative privacy assessment approaches. It aims to highlight the need for balancing privacy with practical requirements in real-world FL scenarios, while also identifying challenges, unresolved issues, and potential areas for future research.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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