Jul 2026· International Journal of Innovative Research in Computer and Communication Engineering· 0 citations
Abstract
Internet of Medical Things (IoMT) applications require collaborative learning across healthcare institutions while ensuring patient data privacy. Traditional centralized learning approaches require sharing sensitive medical records, increasing privacy and security risks. Federated Learning (FL) enables distributed model training by exchanging model parameters instead of raw data, but its performance is affected by client dropouts and communication failures. This paper proposes a Resilience-Enhanced Federated Learning Framework for IoMT applications that improves the reliability of collaborative learning under unstable network conditions. The framework incorporates Federated Averaging (FedAvg), resilient aggregation using historical model updates with staleness decay, and quantized model updates to reduce communication overhead. The proposed model was evaluated using a heart disease dataset distributed across multiple healthcare clients. Experimental results demonstrate that the framework achieves 95.72% accuracy while maintaining stable model convergence during client failures. The proposed approach provides a secure, privacypreserving, and fault-tolerant solution for distributed healthcare applications.
Resource-constrained medical sensing devices are increasingly expected to support local intelligence, privacy-preserving collaboration, and secure communication in Internet of Medical Things (IoMT) environments. However, deploying federated learning in ICU monitoring remains challenging because hospital data are often non-IID, model updates may be adversarially poisoned, and emerging quantum-security threats require stronger communication protection. This paper presents a federated TinyML framework with an edge-hosted patient-state Digital Twin layer for ICU monitoring. The Digital Twin component is implemented as a lightweight patient-state representation rather than a full physiological simulator. It maintains recent physiological observations, temporal risk trends, predicted status labels, and interpretable alert information at the hospital gateway. Patient-specific adaptation is therefore achieved through individualized temporal state tracking and risk-history synchronization, while the predictive model is collaboratively learned across hospitals. The suggested framework employs decision-tree ensembles due to their lightweight nature, efficient inference capabilities, and interpretability inherent to ESP32-class devices. As decision tree structures are not amenable to average aggregation like neural network parameters, the aggregation of local client models is done via the validation-based ensemble fusion. In order to enhance the resistance of the framework to attacks based on poisoning clients, the Performance-Based Filtering (PBF) method analyzes the performance of each local ensemble against a trusted validation set and excludes low-quality local ensembles from the subsequent global ensemble aggregation. The secure model exchange process utilizes ML-KEM-512 key encapsulation scheme in combination with AES-256-GCM authenticated encryption. The extra overhead of the crypto layer is 0.09 ms per update exchange. The framework is evaluated using a clinician-reviewed synthetic ICU monitoring dataset distributed across three non-IID hospital clients and externally benchmarked using a PhysioNet-based critical-care dataset constructed from shared physiological features. The prediction task uses five status categories: normal, mild, moderate, critical, and an outlier/anomaly category; these labels represent synthetic severity-status classes rather than prospectively validated ICU endpoints. Under a targeted label-flipping attack in which one client relabels critical cases as normal, the proposed PBF strategy achieves 86.79% accuracy, 0.782 macro-F1, 0.904 critical-class recall, 0.837 critical-class F1-score, 0.947 macro-AUROC, and 0.799 macro-AUPRC on the synthetic ICU evaluation. External benchmarking at [Formula: see text] achieves 98.04% accuracy, 0.959 macro-F1, 0.977 critical-class recall, 0.987 critical-class F1-score, 0.999 macro-AUROC, and 0.983 macro-AUPRC. Comparative evaluation against FedAvg, Krum, Trimmed Mean, and Coordinate Median shows that PBF provides stronger protection of critical-class recall under adversarial conditions, although full prospective clinical validation remains outside the scope of this study. Overall, the results indicate that a federated TinyML architecture with lightweight patient-state tracking, validation-based ensemble filtering, differential privacy, and post-quantum-secure communication can support privacy-aware and attack-resilient ICU monitoring experiments in resource-constrained IoMT settings. The study should be interpreted as a technical feasibility and robustness evaluation rather than a fully integrated clinical Digital Twin deployment.
Umar Hayat Khan, Rahim Khan, Tahani Alsaedi et al.· Scientific Reports· 0 citations
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.
Mahabala H.N, Seshagiri N· International Journal of Mac...· 0 citations
The rapid integration of Internet of Things (IoT) in the healthcare domain has led to the emergence of the Internet of Medical Things (IoMT), which introduces significant benefits in patient monitoring and real‐time medical services. However, IoMT networks are inherently vulnerable due to resource constraints, heterogeneous devices, and sensitivity of medical data. In this paper, we propose a novel federated learning‐based anomaly detection system (Fed‐ADS) designed specifically for IoMT networks. Our system leverages local training of lightweight ML models on resource‐constrained IoMT devices and employs secure model aggregation at the gateway to preserve privacy and avoid centralized data collection. To address real‐world challenges, we implement and evaluate our system on a real IoMT testbed using Raspberry Pi devices under various attack scenarios. Furthermore, we examine the impact of privacy‐preserving techniques such as differential privacy on detection accuracy and system overhead. The runtime evaluation shows that our approach achieves high detection accuracy (over 94%) with minimal CPU and memory usage (under 3%), making it suitable for practical deployment in medical environments.
Mahdi Ajdani, Maziar Asmani, Asif Ali Laghari· International Journal of Com...· 0 citations
: Federated learning (FL) has emerged as a promising approach for enabling collaborative model training across distributed Internet of Medical Things (IoMT) devices without sharing sensitive data. Existing FL frameworks face significant challenges in healthcare settings, including vulnerability to adversarial attacks, lack of verifiable update integrity, and limited robustness under heterogeneous data distributions. These limitations hinder reliable deployment in critical medical applications. To address these challenges, this paper proposes RP-IoMT, a robust and privacy-preserving FL framework that integrates secure multi-party computation (MPC), zero-knowledge proof-based gradient verification, and robust aggregation mechanisms. The objective of this work is to ensure both the correctness and integrity of model updates while maintaining strong privacy guarantees in adversarial IoMT environments. RP-IoMT enforces bounded client updates using a zero-knowledge clipping protocol (ZKClip), performs secure aggregation using threshold-based MPC, and incorporates robust filtering techniques to mitigate poisoning and backdoor attacks. Experimental results on healthcare datasets demonstrate that RP-IoMT achieves improved predictive performance, reduced attack success rates, and stable convergence under both independent and identically distributed (IID) and non-IID conditions. These results indicate that the proposed framework provides a practical and reliable solution for secure and robust FL in real-world medical Internet of Things (IoT)
M. Ilyas, S. Bhatti, Ghazanfar Latif et al.· Computer Modeling in Enginee...· 0 citations
With the increasing reliance on AI systems operating across multiple cloud infrastructures, ensuring data privacy while enabling efficient collaboration has become a critical challenge. This paper proposes a novel framework for Cross-Cloud Data Privacy Protection by integrating Federated Learning (FL) and Large Language Models (LLMs) to enhance the collaborative mechanisms of AI systems. The framework leverages FL to maintain data locality and preserve privacy, while LLMs act as intelligent coordinators, policy enforcers, and communication optimizers in the federated ecosystem. We present a modular architecture that addresses data heterogeneity, model coordination, and privacy threats. Simulations and case studies demonstrate the feasibility and performance advantages of the proposed approach in real-world scenarios such as cross-institutional healthcare systems. Our findings reveal that combining FL with LLMs can significantly improve security, trust, and operational efficiency in multi-cloud AI collaborations.
Naveen Kumar, M. Krishnan· International Journal of Dat...· 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