Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 461-468· 0 citations· 15 references
Abstract
The increasing adoption of intelligent Android applications in areas such as healthcare, smart transportation, mobile commerce, and personalized services has created significant demand for secure, efficient, and low-latency computing solutions. Traditional cloud-based architectures often face challenges related to communication delays, bandwidth consumption, and user privacy exposure. To address these limitations, this paper proposes a Federated Learning-Based Secure Mobile Edge Computing (FLS-MEC) framework for intelligent Android applications. The proposed framework integrates federated learning with mobile edge computing to enable collaborative model training without transferring sensitive user data to centralized servers. Security mechanisms including secure aggregation, differential privacy, and trust-based validation are incorporated to protect model updates and enhance system reliability. The framework supports efficient edge intelligence by reducing computational latency and communication overhead while maintaining high prediction accuracy. Experimental evaluation demonstrates that the proposed approach significantly improves privacy preservation, resource utilization, and overall system performance compared to conventional cloud-centric solutions. The results indicate that the FLS-MEC framework provides a scalable, secure, and intelligent environment for deploying next-generation Android applications in dynamic mobile computing ecosystems.
Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.
Seshagiri N· International Journal of Mod...· 0 citations
This paper presented an Intelligent Edge Computing Framework for Secure, Low-Latency, and Energy-Efficient Smart Devices that integrates edge intelligence, adaptive task scheduling, resource-aware computation, secure communication, and cloud-assisted services to address the limitations of conventional cloud-centric architectures. By processing data closer to smart devices, the proposed framework significantly reduces latency, minimizes network bandwidth consumption, improves resource utilization, and enables faster real-time decision-making while ensuring data privacy and security. The experimental results demonstrate superior performance in terms of classification accuracy, ROC-AUC, Average Precision, execution time, and computational efficiency compared with existing cloud-based and edge computing approaches. The proposed framework provides a scalable, reliable, and energy-efficient solution for diverse Internet of Things (IoT) applications, including smart healthcare, industrial automation, intelligent transportation, and smart homes. Future work will focus on integrating federated learning, blockchain-enabled security, and next-generation 6G edge intelligence to further enhance scalability, privacy preservation, and autonomous decision-making in large-scale smart device ecosystems.
Kolipaka Vinay, Valusa Venkat Sai Kumar, D. A. Kumar· International Journal of Sci...· 0 citations
This study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework, suitable for applications including healthcare, IoT, smart manufacturing, transportation, and financial fraud detection, providing a secure and scalable solution for next-generation distributed intelligent systems.
Mahabala H. N., Seshagiri N· International Journal of Mac...· 0 citations
This paper proposes a Federated Learning Framework for Privacy-Preserving Smart Infrastructure Monitoring (FL-PSIM), which enables decentralized model training without transferring raw infrastructure data and optimizes global learning while maintaining local data privacy.
Mahabala H. N.· International Journal of Mod...· 0 citations
This study demonstrates the efficacy of the synergy between federated learning and edge computing in IoT security contexts, providing a scalable and privacy-centric solution for anomaly detection across large-scale distributed devices.
Quan Liu, Yuanyuan Feng· Discover Artificial Intellig...· 0 citations
INTRODUCTION: Heterogeneous edge computing creates data islands due to privacy and environmental heterogeneity. Current solutions lack an overall approach. OBJECTIVES: This study constructs a federated learning framework for secure data element circulation, mitigating low-power node tailing, balancing communication with accuracy, and adapting to non-IID data, advancing intelligent systems and cybersecurity. METHODS: The framework fuses Dynamic Clustering, Adaptive Gradient Transmission, and Data Distribution Alignment. Validation uses simulations against existing technologies. RESULTS: The proposed method achieves 0.892±0.021 comprehensive performance, 11.1%–13.6% higher than existing technologies. Under 20% network interruption, attenuation is 3.3% vs. 8.6%–12.5%. Data flow reaches 18.6±0.7 MB/s; privacy leakage is 0.8±0.2 bit. CONCLUSION: This study provides reliable support for safe, efficient data element circulation, advancing intelligent systems and cybersecurity.
Chen Geng, Jianbo Liu· ICST Transactions on Scalabl...· 0 citations
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