FL-MAC-RDP: Federated Learning over Multiple Access Channels with Rényi Differential Privacy

Federated Learning (FL) is a promising paradigm, where the local users collaboratively learn models by repeatedly sharing information while the data is kept distributing on these users. FL has been considered in multiple access channels (FL-MAC), which is a hot issue. Even though FL-MAC has many advantages, it is still possible to leak privacy to a third party during the whole training process. To avoid privacy leakage, we propose to add Rényi differential privacy (RDP) into FL-MAC. At the same time, to maximize the convergent rate of users under the constraints of transmission rate and privacy, the quantization stochastic gradient descent (QSGD) is performed by users. We also illustrate our results on MNIST, and the illustration demonstrate that our scheme can improve the model accuracy with a little loss of communication efficiency.