Abstract

Federated Learning (FL) and blockchain technology have emerged as transformative solutions to current challenges. FL offers a decentralized machine-learning approach, prioritizing privacy, while blockchain promises transparency, security, and decentralization for transactions and data storage. FL enables machine learning models to train across multiple devices while keeping data localized, enhancing data privacy and security. Additionally, FL allows for efficient utilization of computational resources by distributing the training process among participating devices. This not only reduces the burden on individual devices but also enables faster model training and improved accuracy through collaborative learning.Blockchain, originally designed for cryptocurrencies like Bitcoin, creates immutable, transparent, and secure records in a decentralized manner, fostering trust in peer-to-peer networks.The integration of federated learning (FL) and blockchain has the potential to revolutionize data privacy, security, and transparency. By combining FL's ability to train models across multiple devices with blockchain's immutable and decentralized record-keeping, a new paradigm of secure and trustworthy machine learning can be achieved. However, challenges and considerations need to be addressed to fully realize the potential of these technologies.This literature review explores this intersection, including its intricacies, potential, challenges, and future trajectory.