Blockchain-Enabled Trustworthy Federated Learning for Privacy- Preserving Artificial Intelligence: Consensus Protocols, Cryptographic Security, Scalability, and Real-World Deployment

Abstract

Federated learning helps in collaborative training of AI models without transmitting raw data, and hence it is an exciting approach for privacy-conscious applications. Nonetheless, traditional federated learning still suffers from weaknesses such as susceptibility to attacks from malicious actors, susceptibility to model poisoning, single point of coordination, lack of transparency, and scalability issues. Blockchain technology provides a decentralized method of trusting which can help in enhancing the security, accountability, and integrity of federated learning. This paper presents a comprehensive analysis of blockchain based trustworthy federated learning through the examination of the role played by consensus algorithms, cryptographic security techniques, scalability approaches, and deployment strategies. A systematic literature review was conducted using recently published papers from peer-reviewed sources. The selected papers were analyzed using inclusion and exclusion criteria to reveal technological advancements, deployment problems, and practical applications. Consensus methods, which include Proof of Stake, Practical Byzantine Fault Tolerance, Delegated Proof of Stake, and hybrid versions, illustrate various tradeoffs between security, processing speed, latency, and energy utilization. Encryption algorithms like homomorphic encryption, secure multiparty computation, differential privacy, and digital signatures offer added value to privacy and resilience against malicious activity. The review presents several shortcomings concerning communication overheads, blockchain storage expansion, delays in the consensus process, interoperability, and resource-limited edge computing nodes. Recent innovations, such as lightweight consensus, hierarchical blockchain structures, off-chain storage, and adaptive communication models, reveal high prospects in addressing those shortcomings. This research reveals that federated learning by blockchain is a robust and scalable platform to enable privacy-preserving artificial intelligence in healthcare, finance, IoT, smart city, and industrial applications.