Federated Learning and Blockchain: A Cross-Domain Convergence
Shaive Sharma, A.K. Pandey, Vandana Sharma, Sushruta Mishra, Ahmed Alkhayyat
Abstract
Gaining significant attention within decentralized contexts, Federated Learning (FL) has been positioned as a highly desirable method for machine learning. By enabling multiple entities to train a shared model cooperatively, data privacy and security are preserved by Federated Learning. Harnessing inherent transparency and accountability of blockchain technology to trace and authenticate updates effectively in federated learning has transpired as an up-and-coming avenue to tackle data challenges related to confidentiality, protection, and reliability. This study examines the viability of federated learning and blockchain integration across multiple dimensions. The technological components of this integration., including incentive systems, consensus mechanisms, data validation, and smart contracts, are delved into. In the study, a novel proposed model for federated learning integrated with blockchain is designed and implemented. It is observed that the mean cypher size is 100 bytes for varying values of gradients. The average throughput recorded is 1.7 bytes per second, while the mean accuracy is 87.1% for 50 epochs.