Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints

Understand

In this paper, the problem of federated learning (FL) through digital communication between clients and a parameter server (PS) over a multiple access channel (MAC), also subject to differential privacy (DP) constraints, is studied.

• More precisely, we consider the setting in which clients in a centralized network are prompted to train a machine learning model using their local datasets.
• The information exchange between the clients and the PS takes places over a MAC channel and must also preserve the DP of the local datasets.
• Accordingly, the objective of the clients is to minimize the training loss subject to (i) rate constraints for reliable communication over the MAC and (ii) DP constraint over the local datasets.