Blockchain-Enabled Federated Learning for Privacy-Preserving AI Applications

Abstract

The seamless adoption of the artificial intelligence (AI) in some sensitive areas has heightened discomforts on the privacy of data, security and confidence. Another alternative is Federated Learning (FL), an alternative dedicated to centralized model training and data localization prevention, therefore, eliminating exposure risks. Despite these advantages, FL is also marked by such vulnerabilities as model poisoning, unreliability aggregation, and centralized-coordinator dependency. This paper outlines the current difficulties, and presents a federated learning system with a blockchain-based solution to the current issues - via immutability, decentralization, and transparent use of blockchains. Smart contracts will be used to govern the secure verification of participants, fair incentive systems and the impossibility to tamper with updates to models. Such an integration will allow trustless cooperation between untrusted parties and protect sensitive data. The suggested framework is evaluated in a variety of application permutations of AI, such as healthcare, finance, and IoT environments. Experimental results show that by means of blockchain-enabled FL, the privacy-preserving property is represented, robustness against malicious attacks is boosted and scaleable and trust-sensitive AI solutions are developed. The converge opens the path to sustainable, privacy-preserving and secure AI applications in the real world.