Most existing studies on FEEL ignore inter-cell interference and focus on single-cell networks with one FEEL task. This chapter explores FEEL in a multi-cell wireless network, where each cell performs a unique FEEL task using AirComp for fast uplink gradient aggregation. We analyze the convergence of AirComp-assisted FEEL systems, considering inter-cell interference in both downlink and uplink transmissions. It turns out that distorted model/gradient exchanges hinder FEEL from converging by generating error-induced gaps. By intorducing the Pareto boundary of these gaps, we formulate an optimization problem to minimize these gaps across all cells and introduce a cooperative multi-cell FEEL optimization framework to solve the problem by designing downlink and uplink transmissions. Simulations demonstrate that the proposed approach significantly enhances average learning performance across multiple cells compared to non-cooperative methods.