To achieve low-latency and highly-private FEEL over wireless networks, this chapter introduces an innovative approach that integrates RIS with over-the-air FEEL. This approach not only leverages AirComp for efficient model aggregation, but also utilizes RIS to balance learning accuracy and privacy via improving channel conditions. We evaluate the convergence of a differentially private federated optimization algorithm and formulate an optimization problem to enhance learning accuracy while adhering to privacy and power constraints. To solve this problem, we implement a joint design for transmit power, artificial noise, and RIS phase shifts, utilizing a two-step alternating minimization approach. Simulation results validate the effectiveness of this approach, showcasing that RIS can well balance the trade-off between privacy and accuracy as well as demonstrating the practical benefits of our theoretical and algorithmic contributions.