Yu Han

Heng Wang, Haoyu Song, Jiatao Zheng et al.

Magnetic navigation systems, including magnetic tracking systems and magnetic actuation systems, have shown great potential for occlusion-free localization and remote control of intracorporeal medical devices and robots in minimally invasive medicine, such as capsule endoscopy and cardiovascular intervention. However, the design of magnetically navigated robots remains heavily reliant on experimental prototyping, which is time-consuming and costly. Furthermore, there is a lack of a consistent experimental environment to compare and benchmark the hardware and algorithms across different magnetic navigation systems. To address these challenges, we propose the first universal open-source simulation platform to facilitate research, design and benchmarking of magnetically navigated robots. Our simulator features an intuitive graphical user interface that enables the user to efficiently design, visualize, and analyze magnetic navigation systems for both rigid and soft robots. The proposed simulator is versatile, which can simulate both magnetic actuation and magnetic tracking tasks in diverse medical applications that involve deformable anatomies. The proposed simulator provides an open development environment, where the user can load third-party anatomical models and customize both hardware and algorithms of magnetic navigation systems. The fidelity of the simulator is validated using both phantom and ex vivo experiments of magnetic navigation of a continuum robot and a capsule robot with diverse magnetic actuation setups. Three use cases of the simulator, i.e., bronchoscopy, endovascular intervention, and gastrointestinal endoscopy, are implemented to demonstrate the functionality of the simulator. It is shown that the configuration and algorithms of magnetic navigation systems can be flexibly designed and optimized for better performance using the simulator.

Xiaoli Tang, Han Yu, Xiaoxiao Li

Auction-based Federated Learning (AFL) has attracted extensive research interest due to its ability to motivate data owners (DOs) to join FL through economic means. While many existing AFL methods focus on providing decision support to model users (MUs) and the AFL auctioneer, decision support for data owners remains open. To bridge this gap, we propose a first-of-its-kind agent-oriented joint Pricing, Acceptance and Sub-delegation decision support approach for data owners in AFL (PAS-AFL). By considering a DO's current reputation, pending FL tasks, willingness to train FL models, and its trust relationships with other DOs, it provides a systematic approach for a DO to make joint decisions on AFL bid acceptance, task sub-delegation and pricing based on Lyapunov optimization to maximize its utility. It is the first to enable each DO to take on multiple FL tasks simultaneously to earn higher income for DOs and enhance the throughput of FL tasks in the AFL ecosystem. Extensive experiments based on six benchmarking datasets demonstrate significant advantages of PAS-AFL compared to six alternative strategies, beating the best baseline by 28.77% and 2.64% on average in terms of utility and test accuracy of the resulting FL models, respectively.