Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training between the data and the task parties with different features about the same user set while preserving data privacy. In a production environment, VFL usually involves one task party and one data party. Fair and economically efficient feature trading is crucial to the commercialization of VFL, where the task party is considered the data consumer who buys the data party's features. However, current VFL feature trading practices often price the data party's data as a whole and assume transactions occur before performing VFL. Neglecting the performance gains resulting from traded features may lead to underpayment and overpayment issues. In this study, we propose a bargaining-based feature trading approach in VFL to facilitate economically efficient transactions. Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties. We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives. Moreover, we develop performance gain estimation-based bargaining strategies for imperfect performance information scenarios and discuss potential security concerns and solutions. Experiments on three real-world datasets demonstrate the effectiveness of the proposed bargaining model.

In automated warehouses, efficient management and economic benefits hinge on the effective scheduling of automated guided vehicles (AGVs) to transport diverse packets. Emerging technologies such as artificial intelligence and automation control have greatly contributed to the development of packet transport schemes for AGVs. However, the development of the logistics industry results in a massive amount of packets with diverse deadlines, which brings new challenges for the AGV scheduling system. To address this, this paper treats each AGV as an agent and designs a novel hierarchical transformer reinforcement learning (HTRL) framework to generate efficient AGV scheduling policies. Specifically, this framework consists of one encoder and two decoders to produce the packet selection and path improvement actions. These two decoders are equipped with masked self-attention mechanisms to learn efficient packet selection and path improvement policies, facilitating AGV transport efficiency to meet the deadlines of packets. Moreover, we consider the kinetic features of AGVs and design a model predictive control (MPC)-based speed control method for AGVs to prevent frequent stop-and-wait of AGVs and enhance their transport efficiency. We build up a simulated warehouse environment containing packets with different deadlines and conduct extensive experiments. Experimental results validate that the proposed HTRL framework increases the delivered packets within expiration by up to 36.6% compared to other baselines.