Dynamic Cohort Formation with Hierarchical Blockchain Using GDP for Enhanced FL

Abstract

Federated Learning (FL) can be defined as an effective solution for using the benefits of machine learning (ML) in distributed systems, in which the data of the clients remain protected. However overlaid challenges affecting today’s FL systems are centered on time optimization, scalability, and security. To these challenges, this paper responds with a new solution comprising the dynamic cohort creation via smart contracts and a hierarchical blockchain approach. Firstly, the research utilizes smart contracts for the dynamic tracking of cohorts in real time and addresses a two-tier blockchain structure for safety and efficiency in storage. In addition, Gaussian Differential Privacy (GDP) is used as a privacy-preserving mechanism that adds controlled noise to the data or model updates to protect individual data points from being inferred by adversaries. The method we are proposing includes four practical steps that include efficient update validation and aggregation; this will enhance training time, and increase model accuracy as well as data confidentiality. The standard dataset is used to show enhanced performance and scalability which validates this method. Based on the above investigations, it could be concluded that the proposed approach improves FL efficiency and creates a new direction in the development of secure, accurate, and scalable ML. The present study indicates that the implementation of blockchain with FL fortified by GDP will establish a novel innovation between intelligent and safe Artificial Intelligence (AI) architecture for safeguarding the privacy of ML system.