Advancing Federated Learning Privacy With Quantum Communication Techniques: A Robust Scalable Framework
Abstract
Federated learning (FL) provides a collaborative model allowing multiple entities to collectively train artificial intelligence (AI) systems while minimizing data sharing to enhance privacy. Despite its advancements, traditional FL still faces security issues during the exchange of model parameters. The advent of quantum networks, which offer inherent security benefits, prompts us to introduce a new paradigm: quantum communication-based FL (QC-FL). This robust, scalable framework improves data control and mitigates security risks associated with parameter exchange. Our study outlines the key technologies and architecture underpinning QC-FL, illustrating its utility in safeguarding sensitive patient data and enhancing intelligent transportation systems.
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