Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach

no code implementations • 26 Aug 2023 • Jinglong Shen, Xiucheng Wang, Nan Cheng, Longfei Ma, Conghao Zhou, Yuan Zhang

As a promising paradigm federated Learning (FL) is widely used in privacy-preserving machine learning, which allows distributed devices to collaboratively train a model while avoiding data transmission among clients.

Federated Learning Privacy Preserving

Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication

no code implementations • 5 Jul 2023 • Yunhao Quan, Nan Cheng, Xiucheng Wang, Jinglong Shen, Longfei Ma, Zhisheng Yin

Unmanned aerial vehicles (UAVs) have gained popularity due to their flexible mobility, on-demand deployment, and the ability to establish high probability line-of-sight wireless communication.

Collision Avoidance Explainable Artificial Intelligence (XAI)