A Paradigm Shift: Blockchain-Driven Federated Learning

Abstract

Blockchain-driven Federated Learning (BFL) represents an intriguing intersection of two cutting-edge technologies: blockchain and federated learning. A form of distributed machine learning technique known as Federated Learning (FL) aims to preserve the privacy of user data. FL supports privacy preservation, decentralization, and collaborative learning by the means of retaining user data on local devices, training the models without sharing raw data, minimizing the danger of leakage of user data, and avoiding the need for centralized data storage. Beyond these attractive features held by FL, arduous challenges like ensuring secure model aggregation and communication, failure of single points, vulnerability faced by centralized parameter servers, minimal client participation due to lack of motivation, and incentives lacking are encountered. To provide a solution for these obstructions, an innovative idea is to integrate FL with blockchain, which is another decentralized cutting-edge technology. This collaboration leads to a much more robust BFL. FL can be enhanced through blockchain via data provenance where blockchain records data origins as well as model updates by using consensus mechanisms. The consensus mechanisms here ensure the decentralized model integrity, and then the Smart Contracts ensure the automated reward distribution to incentivize participation. FL and blockchain technology use cases are mostly involved in sectors like healthcare, finance, transportation, smart cities, etc. independently. These two core technologies, FL and blockchain, are constructively combined to achieve inviolable higher-end applications, which promise minimized data leakage risk in collaborative data sharing.