Bibliometric Analysis of Literature Based on Blockchain-Based Federated Learning for Privacy-Preserving AI Models

Abstract

Federated Learning (FL) revolutionized the field preserving machine learning by facilitating collaborative model training among decentralized clients in absence of raw data. The classic architectures of FT, in contrast, usually rely on a centralized aggregator, which poses threats such as single points of failure, data poisoning, and model inversion attacks. Use of combination of Blockchain technology holds the promise solution via replacement of centralized aggregators with decentralized consensus mechanisms, improving trust, transparency, and data integrity. The present bibliometric analysis considers the correlation of Blockchain and Federated Learning (BFL), with special reference on flagship aggregation algorithms like FedAvg, FedProx, and FedBN, specifically the blockchain networks such as Ethereum, Hyper- ledger Fabric, and Polkadot. Additionally, the paper records actual- world use cases in privacy-sensitive applications like healthcare, finance, and IoT, using benchmark datasets such as MIMIC-III, NASDAQ stock data, and EdgeIIoTset. The proposed study identifies Key trends, timeless findings, and future directions In BFL, gaining perceptual insights of its growing significance for building trustworthy, privacypreserving AI systems.