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Federated Frameworks: Pioneering Secure and Decentralized Authentication Systems
Abstract
Federated Learning (FL) is innovative machine learning approach that lets multiple devices work together to train models without sharing sensitive data. By keeping data on the device, FL not only boosts privacy and security but also helps improve models collectively. Recent research looked into how Blockchain technology could strengthen FL, tackling existing security issues. Blockchain adds a safeguard against threats like data tampering or unauthorized access and makes systems more transparent and fairer by improving how records and rewards are managed. By blending Blockchain with FL, we get a more trusted and secure way to collaborate on machine learning, bringing both privacy and efficiency to a whole new level.
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