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Adaptive Federated Learning with Privacy Preservation for Robust Anomaly Detection in Multi-Cloud Environments

by Adithya Jakkaraju
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 27
Year of Publication: 2025
Authors: Adithya Jakkaraju
10.5120/ijca2025925479

Adithya Jakkaraju . Adaptive Federated Learning with Privacy Preservation for Robust Anomaly Detection in Multi-Cloud Environments. International Journal of Computer Applications. 187, 27 ( Aug 2025), 31-37. DOI=10.5120/ijca2025925479

@article{ 10.5120/ijca2025925479,
author = { Adithya Jakkaraju },
title = { Adaptive Federated Learning with Privacy Preservation for Robust Anomaly Detection in Multi-Cloud Environments },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2025 },
volume = { 187 },
number = { 27 },
month = { Aug },
year = { 2025 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number27/adaptive-federated-learning-with-privacy-preservation-for-robust-anomaly-detection-in-multi-cloud-environments/ },
doi = { 10.5120/ijca2025925479 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-08-02T01:56:29.621130+05:30
%A Adithya Jakkaraju
%T Adaptive Federated Learning with Privacy Preservation for Robust Anomaly Detection in Multi-Cloud Environments
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 27
%P 31-37
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Multi-cloud deployments face significant security challenges due to fragmented visibility and regulatory constraints on data sharing. This paper proposes a novel Federated Learning (FL) framework for privacy-preserving anomaly detection across heterogeneous cloud environments. The proposed approach combines adaptive federated aggregation (AFA) with a hybrid CNN-LSTM model, differential privacy, and homomorphic encryption to address non-IID data distributions, communication overhead, and privacy risks. Evaluations using synthesized AWS, Azure, and GCP workload traces demonstrate 92.3% F1-score (13.7% improvement over FedAvg) while reducing communication overhead by 63% and resisting model inversion attacks with ε=1.0 differential privacy. The framework maintains compliance with GDPR/HIPAA by design, eliminating raw data transmission. Comparative analysis reveals 28% faster convergence than centralized approaches in asymmetric network conditions, establishing FL as a viable paradigm for cross-cloud security analytics.

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Index Terms

Computer Science
Information Sciences

Keywords

Federated Learning Anomaly Detection Multi-Cloud Security Privacy Preservation Non-IID Data Differential Privacy

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