Next-Generation Cloud Data Platforms Using Federated Learning and Adaptive Analytics for Intelligent Automation

Michael Kimwele; Michael Kimwele

doi:10.15662/IJSRAT.2024.0706019

Next-Generation Cloud Data Platforms Using Federated Learning and Adaptive Analytics for Intelligent Automation

Michael Kimwele

School of Computing & Information Technology, Jomo Kenyatta University of Agriculture & Technology, Nairobi, Kenya

Abstract

Next-generation cloud data platforms are evolving beyond centralized storage and conventional analytics by integrating federated learning, adaptive analytics, and intelligent automation. Increasing volumes of distributed enterprise data create significant challenges involving privacy, scalability, heterogeneous data sources, computational costs, and real-time decision-making. Federated learning enables multiple cloud environments or organizational units to collaboratively train machine learning models while retaining sensitive data locally. Adaptive analytics complements this approach by continuously monitoring data patterns, model performance, and operational conditions to dynamically adjust analytical processes. This research proposes an integrated cloud data platform that combines federated learning with adaptive analytics and intelligent orchestration for secure, scalable, and automated enterprise data intelligence. The framework incorporates distributed data ingestion, local model training, privacy-aware aggregation, adaptive model optimization, workload monitoring, and policy-driven cloud resource management. The proposed methodology evaluates predictive accuracy, processing latency, communication overhead, resource utilization, scalability, and privacy preservation across different experimental configurations. The framework is designed to support dynamic enterprise environments in which data distributions, workloads, and operational requirements continuously change. By reducing unnecessary data movement and enabling intelligent adaptation, the proposed architecture aims to improve analytical efficiency while maintaining data governance and security. The research provides a comprehensive foundation for developing resilient cloud data platforms capable of delivering decentralized intelligence, continuous optimization, and autonomous data-driven decision support across heterogeneous enterprise environments.

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2024.0706019
Pages	13338-13346
Published	November 20, 2024
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Michael Kimwele (%2024). Next-Generation Cloud Data Platforms Using Federated Learning and Adaptive Analytics for Intelligent Automation. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13338-13346. https://doi.org/10.15662/IJSRAT.2024.0706019

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