FSSL-Integrated Consumer Electronics: Ensuring Secure and Efficient AI in Biomedical and Healthcare Applications

The integration of consumer electronics into healthcare and biomedical systems has revolutionised patient care by making personalised diagnostics and real-time monitoring possible. Healthcare data is generated in vast quantities by devices such as wearables and mobile health apps, which increase accessibility and efficiency. However, issues with data security, privacy, and dependability are also brought up by this decentralisation. However, the decentralized nature of consumer electronics introduces critical security and privacy concerns, including unauthorized access, data breaches, and adversarial attacks. The sensitive nature of healthcare data necessitates robust encryption, privacy-preserving mechanisms, and secure model training to ensure confidentiality and reliability. Artificial Intelligence (AI) has developed as an efficient tool for depicting these challenges through advanced learning algorithms, anomaly detection techniques, and privacy-preserving federated learning frameworks. In this research, a Federated Semi-Supervised Learning (FSSL) model is proposed, integrating Federated Optimization (FedOpt), Homomorphic Encryption (HE), and Differential Privacy (DP) to enhance security and efficiency. The model achieves 98.1% accuracy, with a 30% reduction in communication overhead and a 20% improvement in energy efficiency compared to traditional FL approaches. These results highlight the effectiveness of FSSL in securing consumer electronics-based healthcare applications, ensuring real-time, privacy-preserving, and scalable AI-driven medical solutions.