2025· International Journal of Modern Research in Science & Engineering· 0 citations
TL;DR
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.
Abstract
Smart cities, intelligent transportation systems, and industrial infrastructures increasingly rely on IoT, edge computing, and AI to enable real-time monitoring and predictive maintenance. However, centralized machine learning raises concerns regarding data privacy, communication overhead, security, and data ownership. 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. Edge devices collaboratively share encrypted model updates using secure aggregation, differential privacy, and adaptive encryption techniques to preserve confidentiality. The framework also incorporates edge-cloud collaboration to balance computational efficiency, model accuracy, and network resource utilization. Designed to support heterogeneous sensor environments across transportation, energy, industrial, and urban systems, FL-PSIM optimizes global learning while maintaining local data privacy. Experimental results demonstrate improved monitoring accuracy, anomaly detection, communication efficiency, scalability, and resilience against cyber threats compared with centralized AI approaches. The proposed framework provides a secure, privacy-preserving, and scalable foundation for next-generation smart infrastructure, supporting sustainable digital transformation, smart cities, and Industry 5.0 applications.
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 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
A hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning is proposed and can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency.
Huan Yin, Cong Chen, Jing-Yi Zhang et al.· Italian National Conference...· 0 citations
Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequently lack accuracy, scalability, or practicality in their operations. To overcome these limitations, a federated deep learning model is formulated so that secure joint learning can occur without transferring raw data across the domains of ownership. The framework incorporates training that is decentralized, training that uses differential privacy, training that uses secure aggregation, training that uses encrypted communication, and training that uses trust-based anomaly defense to defend against leakage, poisoning, and inference attacks. It also supports heterogeneous and highly non-IID datasets using adaptive coordination and stability-relevant participation regulation and meets emerging data protection requirements. The methods of resource-conscious orchestration and the optimization of communication eliminate overhead without obstructing the effectiveness of learning. The paradigm has therefore formed a privacy-by-design intelligent cloud ecosystem which ensures confidentiality, maintains performance, enhances robustness, and ensures responsible AI implementation in privacy-related sectors of healthcare, finance, governance, and smart infrastructure.
Sribidhya Mohanty, Pallavi Gupta, Anil Pratap Singh et al.· 2026 International Conferenc...· 0 citations
The research findings suggest that improved federated learning can achieve an optimal predictive performance, privacy protection, and secure collaborative learning, which makes it a viable method for next-generation distributed AI systems.
Sheetal Bawane, Leeladhar Chourasiya, S. Jain et al.· International journal of com...· 0 citations
The proposed framework introduces several innovative features, such as federated learning with momentum-based optimization, adaptive differential privacy, trust verification via blockchain, and Byzantine-resilient aggregation, to enhance the security, scalability, and robustness of the system compared with traditional intrusion detection systems.
M. Ramzan· Journal of Communications· 0 citations
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