Secure and Adaptive Task Offloading in Mobile Cloud Computing: A Blockchain-Enabled Federated Deep Learning Framework with hybrid Optimization

Abstract

Mobile Cloud Computing (MCC) enhances resource-constrained mobile devices by enabling task offloading to edge and cloud environments, but faces challenges in security, scalability, and dynamic resource management. This study proposes a secure, blockchain-enabled federated deep learning framework integrated with Hierarchical Adaptive Scalable Spiking Reinforcement Learning (HASSRL) for intelligent and privacy-preserving task offloading. A hybrid optimization approach combining Revolution Optimization Algorithm (ROA) and Proximal Policy Optimization (PPO) is introduced to achieve adaptive scheduling, efficient resource allocation, and real-time decision-making. The framework incorporates continuous monitoring and a feedback-driven self-optimization mechanism to improve latency, energy consumption, and overall system performance. Experimental evaluation using standard datasets demonstrates that the proposed model achieves high accuracy and efficiency, making it suitable for scalable and real-time MCC applications in next-generation networks. The proposed Blockchain-enabled Federated Deep Learning with HASSRL and ROA–PPO achieved a latency of 48.689 ms, energy consumption of 63.222 mJ, and CPU utilization of 95.705%, while maintaining a throughput of 42.067 Mbps and task completion time of 2.781 s. Through experimental evaluation on the UNSW-NB15 and CSE-CIC-IDS2018 datasets, the framework demonstrated efficient task execution, improved resource utilization, and enhanced adaptability in dynamic MCC environments, ensuring secure, scalable, and real-time intelligent task offloading