Federated Learning for Cross-Cloud System Migrations: A Privacy-Preserving Approach

Krupal Gangapatnam

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 1218-1229

Abstract: This article presents an innovative approach to SAP ERP system migrations using federated learning frameworks, addressing the critical challenges of data privacy and system availability in cross-cloud migrations. The proposed article integrates distributed learning techniques with traditional migration tools to create a robust, privacy-preserving migration framework that significantly reduces system downtime while ensuring data security. The experimental results, conducted across multiple cloud platforms with a 2TB test environment, demonstrate a 40% reduction in migration time compared to conventional methods while maintaining 99.95% system availability. The article incorporates advanced encryption protocols, data anonymization techniques, and sophisticated access control mechanisms, providing comprehensive protection for sensitive enterprise data during migration. The framework's scalability and performance characteristics were validated through extensive testing, showing linear scaling capabilities up to 5TB of data. This article contributes to the field by offering a practical, secure solution for enterprise system migrations that meets modern privacy requirements while improving operational efficiency.

Keywords: Federated Learning Migration; Cross-Cloud Privacy; AP System Migration; Privacy-Preserving Enterprise Systems; Distributed Data Replication (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241061161
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:516

DOI: 10.32628/CSEIT241061161

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