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Vertical federated learning (FL) is a critical enabler for distributed artificial intelligence services in the emerging 6G era, as it allows for secure and efficient collaboration of machine learning among a wide range of Internet of Things devices. However, current studies of wireless FL typically consider a single task in a single-cell wireless network, ignoring the impact of inter-cell interference on learning performance. In this study, we investigate a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted over-the-air computation based vertical FL system in multi-cell networks, in which a STAR-RIS is positioned at the cell edge to assist in the execution of various FL tasks across multiple cells. We establish the convergence of the proposed system and present the Pareto boundary of the optimality gaps to depict the trade-off between different cells. Based on the analysis, we jointly …