Predictive Modelling of Livestock Health using Wearable Sensors

Arsh Nishad — 2026-07-21

Animal welfare, farm productivity, and food security are basically on animal health management. The conventional forms of monitoring make a lot of use of manual surveillance, which takes time and mostly identifies diseases when they are on advanced stages. This paper provides a predictive modelling tool of animal health care utilizing wearable sensors, Internet of Things (IoT), and machine learning tools that deliver real time data of physiological and behavioural aspects of animals like body temperature and activity levels, rumination patterns, and location. The resulting data are then sent via the wireless connections to the edge and cloud systems where the preprocessing and predictive analytics are implemented to detect abnormalities and any health risks. Different machine learning methods are used to predict health conditions and provide early warning to farmers and veterinarians. The experimental outcomes prove that multimodal sensor integration is a better way to predict health conditions and provide an opportunity to intervene before it is too late than traditional methods of monitoring. The suggested system promotes the active disease control, lessening the economic losses, and increasing the accuracy of the livestock farming based on the real-time and data-driven decision-making.

A Comprehensive Review on Privacy-Preserving Federated Learning Frameworks for Healthcare IoT Using Blockchain and Edge Intelligence

Mohammad Taha — 2025-06-27

The fast growth of the healthcare Internet of Things (IoT) solutions resulted in the generation of vast amounts of confidential medical data, with an equally growing concern regarding data protection and data use, amongst other factors. This review article will cover the most cutting- edge developments in federated learning (FL) frameworks for privacy- preserving blockchain integration and edge computing in healthcare. At a theoretical level, it became clear that the current centralised machine-learning methods are associated with the risks of data-privacy loss, regulatory impediments, and resource-constraint costs. In this situation, federated learning emerges as a very promising new paradigm capable of accommodating decentralised model training without raw data sharing, thereby further ensuring data privacy and security. In recent years, several contributions have been made combining secure characteristics for data privacy during training and aggregation and well-known techniques such as Fully Homomorphic Encryption (FHE), Secure Multi-Party Computation (SMPC), and Differential Privacy (DP). Moreover, the paper also touches upon the possibilities of blockchain to create secure, transparent, and immutable updates to the models using methodologies like Proof of Authority (PoA) and Delegated Proof of Stake (DPoS). Moreover, directing edge computing into the design could further enhance systems’ scalability and minimise latency within the healthcare domain. The text addresses the issues like non- IID data distribution, class imbalance, communication overhead, and convergence problems in the federated setting. Emerging methodologies such as cross-domain federated learning, multitask learning, and adaptive aggregation strategies were discussed to enhance the model’s generalisation and efficiency levels. The overall goal of the paper is to propose and develop a combination of federated learning together with blockchain and privacy-preserving technologies to enhance secure, scalable, and efficient health IoT systems.

How to cite this article:
Taha M, Rai A K. A Comprehensive Review on Privacy-Preserving Federated Learning Frameworks for Healthcare IoT Using Blockchain and Edge Intelligence. J Adv Res Cloud Comp Virtu Web Appl 2026; 9(2): 1-10.

Journal of Advanced Research in Cloud Computing, Virtualization and Web Applications

Predictive Modelling of Livestock Health using Wearable Sensors

A Comprehensive Review on Privacy-Preserving Federated Learning Frameworks for Healthcare IoT Using Blockchain and Edge Intelligence