Federated Learning Platform for Edge Cloud Intelligence and Privacy-Preserving Enterprise Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2026.0904004Keywords:
Federated learning, edge computing, cloud intelligence, privacy preservation, enterprise analytics, distributed artificial intelligence, secure aggregation, Internet of Things, machine learning, decentralized systemsAbstract
The rapid expansion of Internet of Things (IoT) devices, edge computing environments, and distributed enterprise systems has created significant challenges in managing large-scale data analytics while preserving user privacy and ensuring computational efficiency. Traditional centralized machine learning approaches require transferring massive volumes of raw data to cloud servers, increasing communication costs, security risks, and regulatory concerns. Federated learning has emerged as a promising paradigm that enables collaborative model training across distributed edge devices without exposing sensitive data. This research explores the development of a federated learning platform for edge cloud intelligence and privacy-preserving enterprise analytics. The proposed platform integrates edge computing, cloud infrastructure, secure aggregation mechanisms, artificial intelligence algorithms, and privacy-enhancing technologies to support decentralized learning environments. The study investigates how federated learning can improve enterprise decision-making by enabling real-time analytics, reducing data transmission requirements, and maintaining confidentiality across organizational boundaries. The research methodology combines architectural analysis, algorithm evaluation, simulation-based experimentation, and performance assessment of federated learning models deployed within edge-cloud ecosystems. Key evaluation factors include model accuracy, communication efficiency, privacy protection, scalability, and computational performance. The findings are expected to demonstrate that federated learning platforms can provide a secure and intelligent foundation for next-generation enterprise analytics by balancing data utility with privacy preservation. This research contributes to the advancement of distributed artificial intelligence systems capable of supporting intelligent applications in healthcare, finance, manufacturing, smart cities, and other data-intensive industries.
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