Privacy-preserving machine learning: a review of federated learning techniques and applications
Abstract
Federated Learning (FL), which permits decentralized model training without sharing raw data, guarantees adherence to privacy laws like GDPR and HIPAA. This study offers a thorough analysis of FL with an emphasis on its exceptional capacity to strike a balance between data value and privacy in industries including healthcare, the Internet of Things, and finance. In contrast to previous evaluations, this study explores sophisticated privacy-preserving techniques, such as differential privacy and homomorphic encryption, and assesses how well they work to handle issues like adversarial threats, non-IID data distributions, and communication overhead. The study also discusses the practical uses of optimization techniques like Federated Proximal (FedProx) and Federated Averaging (FedAvg). This paper provides practical insights and future approaches to promote the use of FL in privacy-sensitive AI applications by comparing and contrasting current methods and pointing out research gaps. FL is positioned as a revolutionary method for privacy-conscious machine learning because to this fresh viewpoint.
This update highlights the paper's distinctive features that set it apart from prior reviews, including the thorough examination of privacy mechanisms, assessment of optimization techniques, and identification of research needs.
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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