Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Federated Learning (FL) has emerged as a powerful approach that enables collaborative model training across decentralized devices while preserving data privacy. However, despite its advantages, FL remains vulnerable to privacy breaches, model inversion attacks, and communication inefficiencies. This study presents an enhanced survey on privacy preservation strategies in federated learning, exploring state-of-the-art techniques such as differential privacy, homomorphic encryption, secure multi-party computation, and blockchain integration. The paper systematically examines their effectiveness, trade-offs, and applicability in various domains. Additionally, it highlights key challenges, including scalability, computation overhead, and trust management among clients. Finally, future research directions are discussed to strengthen the privacy and efficiency of federated learning systems, contributing to the development of secure and trustworthy artificial intelligence.
"An Enhanced Survey on Privacy Preservation Strategies in Federated Learning: Techniques, Challenges, and Future Directions", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.a632-a634, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512079.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator