Enhancing Privacy in Federated Learning through Quantum Teleportation Integration

TL;DR

This paper explores integrating quantum teleportation into federated learning to significantly enhance privacy by securely exchanging model updates and detecting eavesdropping, despite current technological limitations.

Contribution

It proposes a novel architecture combining quantum teleportation with federated learning, leveraging quantum entanglement for secure model parameter exchange.

Findings

Quantum teleportation can secure model updates against eavesdropping.

Integration offers potential for higher privacy levels in federated learning.

Practical challenges include current quantum network infrastructure limitations.

Abstract

Federated learning enables collaborative model training across multiple clients without sharing raw data, thereby enhancing privacy. However, the exchange of model updates can still expose sensitive information. Quantum teleportation, a process that transfers quantum states between distant locations without physical transmission of the particles themselves, has recently been implemented in real-world networks. This position paper explores the potential of integrating quantum teleportation into federated learning frameworks to bolster privacy. By leveraging quantum entanglement and the no-cloning theorem, quantum teleportation ensures that data remains secure during transmission, as any eavesdropping attempt would be detectable. We propose a novel architecture where quantum teleportation facilitates the secure exchange of model parameters and gradients among clients and servers. This integration aims to mitigate risks associated with data leakage and adversarial attacks inherent in classical federated learning setups. We also discuss the practical challenges of implementing such a system, including the current limitations of quantum network infrastructure and the need for hybrid quantum-classical protocols. Our analysis suggests that, despite these challenges, the convergence of quantum communication technologies and federated learning presents a promising avenue for achieving unprecedented levels of privacy in distributed machine learning.