A Novel Architecture for Secure Federated Learning Infrastructures for Quantum-AI Workloads

The rise of hybrid Quantum–AI (QAI) systems require new paradigms for secure, distributed computation. Existing federated learning frameworks depend on centralized coordination and limited encryption, leaving them vulnerable to model inversion, data leakage, and integrity attacks; risks that become critical when handling sensitive information such as healthcare data. Moreover, these frameworks are  not designed to protect quantum-classical workloads that span multiple institutions, devices, and regulatory domains. We introduce a novel hardware–software co-designed architecture for a secure federated learning infrastructure that establishes verifiable trust from edge nodes to quantum backends. The proposed system ensures data privacy at rest, in transit, and during computation through trusted execution environments and cryptographic orchestration, enabling privacy-preserving collaboration on sensitive tasks such as healthcare analytics. This architecture provides a scalable blueprint for secure cross-site QAI research, addressing one of the key bottlenecks to real-world deployment of quantum-enhanced learning. Our results highlight how embedding “trust by design” into every computational layer can accelerate the adoption of Quantum-AI systems in sensitive big data in various applications.