2025· International Journal of Machine Learning and Predictive Analytics· Vol 8, pp. 01-15· 0 citations
TL;DR
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.
Abstract
The rapid growth of IoT, edge computing, healthcare, and cyber-physical systems has increased the need for privacy-preserving distributed analytics. Traditional centralized machine learning requires sharing raw data, creating privacy, regulatory, and communication challenges. Federated Learning (FL) enables collaborative model training without exchanging sensitive data but faces limitations such as Non-IID data, communication overhead, privacy leakage, malicious updates, and inefficient aggregation. To address these issues, this study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework. The framework combines secure local training, privacy-aware weighted aggregation, client trust evaluation, and adaptive optimization to improve prediction accuracy, privacy protection, and communication efficiency. It is 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.
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.
Mahabala H.N· International Journal of Mod...· 0 citations
Internet of Things (IoT), edge computing, and cloud computing has transformed data-driven services across healthcare, transportation, manufacturing, finance, agriculture, and smart cities. These applications generate large volumes of sensitive distributed data, making traditional centralized machine learning unsuitable due to privacy, security, communication, and regulatory challenges. Federated Learning (FL) addresses these issues by enabling collaborative model training without transferring raw data, thereby preserving user privacy.This paper presents a privacy-preserving federated learning framework that integrates secure aggregation, differential privacy, encryption, adaptive communication, and federated optimization for large-scale heterogeneous environments. The framework incorporates edge computing, blockchain, Trusted Execution Environments (TEE), and Explainable AI (XAI) to improve security, transparency, and trust. A multi-layer architecture consisting of client devices, edge servers, federated coordinators, cloud services, and security modules is proposed. Adaptive client selection, weighted federated averaging, dynamic privacy allocation, and asynchronous synchronization are employed to improve learning under Non-IID data distributions. 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
The rapid proliferation of Internet of Things (IoT) devices across smart homes, industrial plants, healthcare systems, and wearable platforms has generated an unprecedented volume of distributed, heterogeneous, and privacy-sensitive data. Conventional centralized anomaly detection pipelines require raw sensor and network traffic data to be transmitted to a central server, exposing sensitive information to interception, misuse, and regulatory non-compliance while also incurring substantial communication overhead. This paper proposes a Federated Learning (FL) framework for privacy-preserving anomaly detection tailored to heterogeneous IoT networks characterised by non-independent and identically distributed (non-IID) data, variable computational capacities, and intermittent connectivity. The proposed architecture couples a lightweight convolutional-recurrent local model with a differential-privacy-augmented Federated Averaging (FedAvg) aggregation strategy and an optional secure-aggregation layer to prevent gradient leakage. A dynamic client-selection and adaptive-weighting mechanism mitigates statistical heterogeneity across device clusters, while a local outlier-scoring module enables edge-level anomaly flagging without exposing raw payloads. Extensive simulation on merged benchmark traffic derived from N-BaIoT and CICIDS2017 characteristics, partitioned across simulated smart-home, industrial, and wearable clusters, demonstrates that the proposed framework attains 97.4%–97.7% detection accuracy, within one to two percentage points of a fully centralized, non-private baseline (98.6%), while reducing raw-data transmission to zero and lowering communication overhead relative to naive parameter exchange. Comparative results against local-only training confirm that federated collaboration yields a 9–10 percentage-point accuracy improvement under severe data heterogeneity. These findings indicate 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
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
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
The rapid growth of the Internet of Things (IoT) in critical domains such as healthcare, smart cities, cybersecurity, and finance has led to the generation of large volumes of distributed data and increased susceptibility to cyberattacks. Although federated learning (FL)-based intrusion detection systems (IDS) have been introduced to support distributed learning and improve privacy, they still face several challenges, including performance limitations, high computation and communication overhead, and potential privacy attacks. To address these challenges, we propose a Random Projection-Based Personalized Federated Learning (RPPFL) framework for IoT intrusion detection. In the proposed framework, random projection, a lightweight one-way dimensionality reduction transformation, is applied at the IoT device level, which enhances privacy while lowering computational and communication costs. Furthermore, personalized federated learning at the fog layer reduces the computational burden on resource-constrained IoT devices and improves model robustness in environments where IoT data are non-independent and identically distributed (non-IID). We also introduce a conditional generative adversarial network (cGAN)-based privacy attack to evaluate the resilience of the proposed framework and demonstrate the effectiveness of our approach in preserving the privacy of IoT data. Experimental results on the RT-IoT 2022 and CIC-IoT 2023 datasets demonstrate that RPPFL provides high detection accuracy (above 95.0%) while preserving data privacy and reducing computation and communication overhead in dynamic IoT environments. The proposed framework is generalizable and applicable to a wide range of IoT intrusion detection scenarios.
Md. Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea et al.· Annual International Compute...· 0 citations