Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-7· 0 citations· 18 references
Abstract
Many Internets of Things (IoT) applications are built on the backbone of a Wireless Sensor Network known as WSNs which are used to collect large amounts of data used in smart cities, healthcare, industrial monitoring, and environmental sensing. Nevertheless, the conventional centralized data analysis systems demand transmission of raw sensor data to the cloud servers, which presents grave issues associated with privacy of data, security, heavy data communication, and energy usage. The concept of Federated Learning (FL) has become one of the most prospective ways to overcome these challenges as it allows collaborative training of models at distributed sensor nodes without exchanging raw data. The paper explores the use of federated learning in place of WSNs in the privacy analysis of IoT data. The suggested framework enables sensor nodes to locally develop machine learning models based on their individual data and transmit only updates to the models to a coordinating server or aggregator. The framework saves sensitive data in the source and thereby provides high protection to privacy and also minimizes the cost of communication. To meet the mobile nature of WSN nodes, the model uses lightweight learning algorithms, energy-sensitive update scheduling, and secure aggregation to build on the resource limitations of a small network node. Performance analysis proves that a federated learning system can reach similar levels of accuracy as those of centralized learning and significantly enhance the degree of privacy of data, decrease the volume of network traffic and increase the lifespan of a network. The findings indicate the viability and efficacy of federated learning as a scalable and secure system of distributed intelligence in WSN-based IoT systems. The current study offers important information in the development of future-generation privacy-sensitive IoT analytics.
The rapid deployment of Internet of Things (IoT) infrastructure in precision agriculture has produced large volumes of distributed sensor data, including soil moisture, ambient temperature, humidity, and crop-health indicators. Although centralized machine-learning pipelines can exploit these data for intelligent decision support, they introduce major concerns related to privacy exposure, transmission overhead, and cyber-security risk. This paper presents FedAgri, a federated learning (FL) framework tailored to agricultural IoT environments. FedAgri enables multiple farm nodes to jointly train a global model for crop-condition prediction without exchanging raw sensor records. To address the heterogeneous and non-independently-and-identically-distributed (non-IID) characteristics of geographically dispersed agricultural data, we introduce a dynamic aggregation mechanism that weights client updates according to local data quality and distribution divergence. In addition, a lightweight differential privacy module is incorporated to provide formal privacy guarantees while preserving model utility. Simulation results on publicly available agricultural datasets show that the proposed framework attains prediction accuracy within 2.3% of a centralized baseline, reduces communication overhead by 68%, and delivers epsilon-differential privacy protection. These findings demonstrate the practical feasibility of privacy-preserving collaborative learning for smart-agriculture applications.
Yuejun Li, Guilan Xiao· International Conference on...· 0 citations
With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly detection, equipment monitoring, and predictive maintenance. Federated learning offers a practical way to train models without exposing raw sensor data, but it still faces privacy leakage and malicious poisoning attacks. To address these issues, this paper proposes a hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning. Starting from the sensitivity differences among parameters at different model layers, the proposed method designs a hierarchical privacy-budget allocation strategy that enhances protection for sensitive information while minimizing the performance impact of perturbation. Meanwhile, a multi-layer, multi-feature anomaly-detection mechanism is adopted to identify malicious updates by jointly exploiting directional consistency, scale stability, and inter-layer similarity, and majority voting together with update clipping is used to further improve system robustness. Experiments on Fashion-MNIST, MVTec AD, and C-MAPSS demonstrate that the proposed method 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
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 work introduces a federated learning framework on campus that provides a resource-conscious multi-layer federating strategy that controls the number of devices taking part in the process as well as the frequency at which aggregation is done based on each device’s capability.
C. Ravi, S. Reddy, S. Bhargav et al.· International Journal of Ele...· 0 citations
Simulation of a Federated Learning framework for privacy-preserving anomaly detection tailored to heterogeneous IoT networks characterised by non-independent and identically distributed data, variable computational capacities, and intermittent connectivity indicates that the proposed method offers a practical, scalable, and regulation-compliant pathway toward trustworthy intrusion and anomaly detection in large-scale, heterogeneous IoT deployments.
Raushan Raj, B. L. Pal, Saurab Singh· International journal of com...· 0 citations
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