Federated Learning in Multi-RIS Aided Systems

Understand

This paper investigates the problem of model aggregation in federated learning systems aided by multiple reconfigurable intelligent surfaces (RISs).

• The effective integration of computation and communication is achieved by over-the-air computation (AirComp).
• Since all local parameters are transmitted over shared wireless channels, the undesirable propagation error inevitably deteriorates the performance of global aggregation.
• The objective of this work is to 1) reduce the signal distortion of AirComp; 2) enhance the convergence rate of federated learning.