This paper comprehensively reviews the application of Federated Learning in fingerprint recognition, a distributed machine learning paradigm that enables collaborative modeling without sharing raw data, thus offering a promising privacy-preserving solution.
FedE, a multi-precision, multi-source, heterogeneous privacy-preserving federated learning training method based on functional encryption that enhances numerical adaptation during ciphertext computation and prevents model parameter updates from easily compromising privacy in cross-institutional federated learning.
Weijia Liu, Junwen Deng, Hao Li et al.· Computers, Materials & C...· 0 citations
Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequent...
Sribidhya Mohanty, Pallavi Gupta, Anil Pratap Singh et al.· 2026 International Conferenc...· 0 citations
It was concluded that strategies such as the careful selection of differential privacy parameters and training settings, along with the use of larger datasets, can improve the efficiency of FL and demonstrate that privacy-preserving and high-performance artificial intelligence systems can be securely applied in sensiti...
Cagdas Karatas, Hibanur Karadogan, A. Ertug et al.· 0 citations
SecureFedShield is proposed, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments that integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture.
Kriti Mishra· International Journal of Cre...· 0 citations
A review of federated learning through a structured taxonomy that covers its core architectural paradigms, major learning types, model training approaches, and aggregation mechanisms, and analyzes the principal challenges confronting FL, including privacy and security risks, statistical and system heterogeneity, commun...
Mahdiyeh Velaei, Hosna Ghahramani, Ali Ghaffari et al.· Cluster Computing· 0 citations
An in-depth analysis of federated learning methods and paying special attention to the issue of privacy is provided, which examines new developments, concerns and tradeoffs connected with privacy, effectiveness of communication, model noise, and scalability of systems.
Aarav Mehta· International Journal of App...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
