Aerial Assistive Payload Transportation Using Quadrotor UAVs with Nonsingular Fast Terminal SMC for Human Physical Interaction

This paper presents a novel approach to utilizing underactuated quadrotor Unmanned Aerial Vehicles (UAVs) as assistive devices in cooperative payload transportation task through human guidance and physical interaction. The proposed system consists of two underactuated UAVs rigidly connected to the transported payload. This task involves the collaboration between human and UAVs to transport and manipulate a payload. The goal is to reduce the workload of the human and enable seamless interaction between the human operator and the aerial vehicle. An Admittance-Nonsingular Fast Terminal Sliding Mode Control (NFTSMC) is employed to control and asymptotically stabilize the system while performing the task, where forces are applied to the payload by the human operator dictate the aerial vehicle's motion. The stability of the proposed controller is confirmed using Lyapunov analysis. Extensive simulation studies were conducted using MATLAB, Robot Operating System (ROS), and Gazebo to validate robustness and effectiveness of the proposed controller in assisting with payload transportation tasks. Results demonstrates feasibility and potential benefits utilizing quadrotor UAVs as assistive devices for payload transportation through intuitive human-guided control. Keywords Cooperative payload transportation, Admittance control, Sliding mode control, Quadrotor control

zkFDL: An efficient and privacy-preserving decentralized federated learning with zero knowledge proof

Federated leaning (FL) has been frequently used in various field of studies and businesses. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years in which with the help of blockchains, try to achieve more integrity and efficiency. On the other hand, privacy-preserving is an uncovered part of these systems. To address this, and also scaling the blockchain-based computations, we propose a zero knowledge proof (ZKP) based aggregator (zkDFL) that allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. The server can also prove that all inputs of clients have been used. We evaluate our measure through a public dataset about wearable internet of things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of correctness of aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has declined significantly.