# Federated Learning over Blockchain-Enabled Cloud Infrastructure

Source: [Federated Learning over Blockchain-Enabled Cloud Infrastructure](<https://www.sota2.com/research/paper/69e9a7a555a3c32af89f70f3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-24T05:00:04.605Z

Authors: Saloni Garg, Amit Sagtani, Kamal Kant Hiran

Published: 2026-04-21T23:51:00.000Z

## Abstract

The rise of IoT devices and the uptake of cloud computing have informed a new era of data-driven intelligence. Traditional centralized machine learning models that require a large volume of data to be stored in a single location have therefore become more susceptible to data breaches, privacy violations, and regulatory non-compliance. This report presents a thorough examination of the merging of Federated Learning (FL) and blockchain technology in a cloud-edge setting, demonstrating it as an effective solution to the stated concerns. We are proposing a detailed four-dimensional architectural categorization that meticulously assesses coordination frameworks, consensus algorithms, data storage practices, and trust models that are significant to these integrated systems. The manuscript presents a comprehensive comparative examination of two cutting-edge frameworks: the Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB), which is designed for intelligent transportation systems, and the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS), elucidating their distinctive contributions and inherent limitations. Lastly, we engage in a thorough evaluation of the literature that integrates a comparative perspective on current frameworks to discern the singular nature of this research within existing knowledge systems. The manuscript culminates in delineating the principal challenges and offering a strategic framework for prospective research trajectories, emphasizing the advancement of adaptive, resilient, and standardized BCFL systems across diverse application domains.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on CIFAR-100 (test) (Accuracy, Latency, Security/Compliance)](<https://www.sota2.com/research/sota/image-classification-on-cifar-100-test-accuracy-latency-security-compliance.md>): Image Classification; CIFAR-100 (test); Average Latency (s): 3.216.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.20062>)

[PDF](<https://arxiv.org/pdf/2604.20062.pdf>)