Federated learning for privacy-preserving iot data aggregation in smart healthcare applications
Abstract
Internet of Things (IoT) gadgets have transformed patient monitoring and individualised treatment through their incorporation into state-of-the-art healthcare systems. Concerns regarding data privacy and security are heightened by the decentralised nature of IoT data collection, as well as by the increasingly inadequate state of traditional centralised machine learning approaches that aggregate sensitive health data on central servers in response to strict privacy regulations such as GDPR and HIPAA. It examined the designs for safe data aggregation techniques like homomorphic encryption and secure multiparty computation, as well as differential privacy, which are safe data aggregation techniques. Also, we talk about incorporating edge and fog computing to improve FL efficiency and scalability within resource-constrained IoT devices. An approach that has some promise is federated learning (FL), which allows distributed devices to train models jointly without explicitly exchanging data. In order to collect data from the Internet of Things (IoT) while safeguarding patient privacy, this article focuses on how smart healthcare settings might utilise FL. The vision overhead, data diversity, and adversarial attacks are critical issues the research tackles that the vision overhead, data diversity, and adversarial attacks are essential issues the research tackles and offers solutions for. By reviewing past research and real-world instances, the study highlights how deeply…
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.