Quantum Federated Learning

EasyChair Preprint 14868

Abstract

Quantum Federated Learning (QFL) represents an innovative convergence of quantum computing and federated learning, aimed at enhancing privacy-preserving machine learning techniques through quantum technologies. Federated learning, a decentralized approach to training machine learning models across multiple devices or data sources, prioritizes data privacy by keeping data local and sharing only model updates. Quantum computing, with its potential for superposition and entanglement, promises to accelerate learning processes and improve model performance in this decentralized paradigm.

This paper explores the foundational principles of Quantum Federated Learning, integrating quantum algorithms with federated learning frameworks to address key challenges such as data privacy, communication efficiency, and model robustness. We discuss the theoretical underpinnings of QFL, including quantum-enhanced optimization techniques and privacy-preserving protocols. Additionally, we examine practical implementations and potential applications, highlighting how QFL can revolutionize fields such as secure multi-party computation, medical data analysis, and collaborative artificial intelligence.

Our findings suggest that QFL could significantly enhance the efficiency and security of federated learning systems, offering a promising direction for future research in both quantum computing and machine learning domains. This paper provides a comprehensive overview of QFL's potential, limitations, and future research avenues, laying the groundwork for subsequent advancements in this emerging interdisciplinary field.

Keyphrases: Quantum Federated Learning, machine learning, privacy preserving

@booklet{EasyChair:14868,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Quantum Federated Learning},
  howpublished = {EasyChair Preprint 14868},
  year      = {EasyChair, 2024}}