Differentially-Private Decentralized Learning in Heterogeneous Multicast Networks

We propose a power-controlled differentially private decentralized learning algorithm designed for a set of clients aiming to collaboratively train a common learning model. The network is characterized by a row-stochastic adjacency matrix, which reflects different channel gains between the clients. In our privacy-preserving approach, both the transmit power for model updates and the level of injected Gaussian noise are jointly controlled to satisfy a given privacy and energy budget. We show that our proposed algorithm achieves a convergence rate of O(log T), where T is the horizon bound in the regret function. Furthermore, our numerical results confirm that our proposed algorithm outperforms existing works.