Auction-Based Trustworthy and Resilient Quantum Distributed Learning

Federated learning (FL) has emerged as a powerful paradigm for decentralized training, particularly in privacy-sensitive fields such as medical Internet of Things (IoT) services, where data security is important. However, FL faces challenges due to nonindependent and identically distributed (non-IID) data among clients, which can lead to suboptimal performance. In addition, there is a risk of data leakage during the aggregation process. To address these issues, we propose a novel approach using an auction mechanism to filter out unreliable clients, ensuring that only trustworthy participants are involved in the learning process. The selected clients are organized in a ring topology, eliminating the need for a central server and thereby reducing the risk of data breaches. Additionally, we leverage quantum neural networks (QNNs) to enhance security further, utilizing the quantum no-cloning theorem to prevent the duplication of quantum parameters. The results demonstrate that our approach can handle non-IID data distributions effectively and improve model performance, even with small and imbalanced datasets.