Trustfed a scalable privacy preserving federated AI framework for industrial IoT healthcare and finance
Dileep Kumar Murala, K Madhura, Veera Ankalu Vuyyuru, K. Varaprasada Rao, Eric Hitimana · Discover Internet of Things · 2026
The integration of AI, IoT, and edge–cloud computing is accelerating smart industrial system improvements, particularly in healthcare and finance. This paper presents TrustFed, a secure and privacy-preserving federated AI platform, to address IIoT data privacy, security, and scalability issues. TrustFed uses Intel SGX–based trusted execution, Federated Deep Learning (FDL), Differential Privacy (DP), PCA-driven feature reduction, and encryption-based secure aggregation for decentralised model training confidentiality and robustness. Two privacy-aware face recognition and brain tumour classification use cases verify the system, showing better accuracy, reduced communication overhead, and robustness to inference and poisoning assaults. TrustFed improves data privacy and performance, adding scientific value to secure AI adoption in large-scale smart industrial environments.