Quantum Walk Based Federated Learning

Abstract

Federated learning (FL) offers a promising approach to distributed machine learning by enabling collaborative model training without directly exchanging raw data. However, traditional FL systems are plagued by privacy vulnerabilities and significant communication costs. This paper proposes a novel framework, Quantum Walk Based Federated Learning (QWFL), that addresses these limitations by integrating the principles of quantum walks. We leverage quantum superposition and interference to create a secure and efficient model aggregation process. The core idea is to represent model updates as quantum walks on a graph representing the distributed devices, where the walk's evolution inherently obfuscates information. This approach significantly reduces the risk of privacy breaches while concurrently minimizing communication overhead. The theoretical analysis demonstrates the potential of QWFL to achieve better privacy guarantees and communication efficiency compared to conventional FL methods. We present a formal mathematical model of the QWFL system and analyze its key properties, including the walk's convergence rate and the level of privacy protection afforded by the quantum walk process. The design aims for practical implementation by considering the challenges of integrating quantum computing technologies into existing FL architectures. The results indicate that QWFL provides a viable pathway towards robust and scalable distributed learning.