Federated Learning in Quantum Computing for Privacy-Preserving and Distributed Quantum Model Training
Purna chander Mashetty, Srikanth Chittipothu, Naga Venkatesh Gangabathula, Sirish Gangabathula, Pruthvi Krishna Gutta, Nithin Aravind Soundarajan Rajalakshmi
Abstract
The mix of quantum computing and machine learning has introduced strong quantum models able to resolve challenging tasks efficiently. Still, the use of centralized QML systems could present challenges with privacy because sharing quantum data with untrusted aggregators is especially risky. This study describes a method called Privacy-Preserving Quantum Federated Learning (PQFL), which allows multiple clients to jointly train quantum models while maintaining data confidentiality. This system offers security by combining QHE and QDP to cover the processes of aggregating protected models and enforcing privacy on the data. Hybrid quantum-classical studies reveal that PQFL is both accurate and advanced in protecting privacy and communication security. According to these results, PQFL works effectively in privacy-friendly domains including healthcare, genomics, and finance, where data needs to be managed securely.