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The study aims to review current approaches to privacy preservation in Federated Learning, identifying challenges and future directions.

A systematic review on privacy preservation in federated learning

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The study aims to review current approaches to privacy preservation in Federated Learning, identifying challenges and future directions.
Conducted a systematic review of literature on Federated Learning and privacy preservation.
Identified 392 papers, narrowing down to 153 eligible publications for analysis.
Analyzed applications, techniques, and models relating to privacy preservation.
Over 90% of analyzed papers were Scopus-indexed Q1 publications.
The review presents significant challenges in implementing privacy measures in Federated Learning.
Findings call for better integration of advanced privacy techniques for effective real-world application.

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This systematic review examines privacy preservation techniques in federated learning, highlighting advancements and challenges.

Sum et al. (2026) studied this question.

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