Mitigating data leaks and optimizing task scheduling in cloud computing using EfficientNetV2 with federated learning and blockchain

doi:10.1007/s10586-025-05282-4

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Mitigating data leaks and optimizing task scheduling in cloud computing using EfficientNetV2 with federated learning and blockchain

2025-08-30

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DOI：10.1007/s10586-025-05282-4

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Abstract

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Cloud computing offers scalable, on-demand services but faces critical challenges related to security vulnerabilities, data leakage, and inefficient task scheduling. Existing solutions often fail to provide a unified approach that ensures both security and optimal resource utilization. This study proposes a novel Secure and Optimized Federated Cloud Framework (SOFCF) that integrates EfficientNetV2-based Federated Learning (FL), Kubernetes-based task scheduling, and blockchain-enhanced security to address these issues. The proposed model prevents data leakage by employing FL for decentralized model training, ensuring data remains local while contributing to a global model. EfficientNetV2 accelerates training convergence, improving classification performance. Kubernetes dynamically schedules tasks based on real-time demands, utilizing a Multi-Armed Bandit (MAB) fine-grained resource allocation strategy to optimize CPU and memory usage. EfficientNetV2 accelerates training convergence, achieving 99.13% classification accuracy on the CVC3345 dataset, outperforming existing models. The proposed model improves resource utilization by 19%, reduces data leakage incidents by 85%, and reduces makespan by 28%. The proposed model reduces response time significantly, achieving 155 ms, showcasing its efficient task scheduling and resource management. This research establishes a trustworthy, decentralized, and efficient cloud computing model, ensuring privacy-preserved and optimized resource management in distributed environments.

Keywords:

Blockchain

Cloud computing

EfficientNetV2

Federated Learning

Kubernetes

Task Scheduling