DT-QFL: Dual-Timeline Quantum Federated Learning With Time-Symmetric Updates, Temporal Memory Kernels, and Reversed Gradient Dynamics

Abstract: Federated learning has emerged as a powerful paradigm for decentralized model training, ensuring privacy preservation by allowing clients to collaboratively learn a shared model without exchanging raw data. Quantum federated learning (QFL) extends this approach by leveraging quantum computing to enhance computational efficiency and security. However, existing QFL frameworks face challenges in handling temporal […]