Boosting Fairness and Robustness in Over-the-Air Federated Learning: Convergence Properties

27 Oct 2024

Authors:

(1) Halil Yigit Oksuz, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany;

(2) Fabio Molinari, Control Systems Group at Technische Universitat Berlin, Germany;

(3) Henning Sprekeler, Exzellenzcluster Science of Intelligence, Technische Universit¨at Berlin, Marchstr. 23, 10587, Berlin, Germany and Modelling Cognitive Processes Group at Technische Universit¨at Berlin, Germany;

(4) Jorg Raisch, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany.

Table of Links

Abstract and Introduction

Problem Setup

Federated fair over-the-air learning (FedAir) Algorithm

Convergence Properties

Numerical Example

Conclusion and References

IV. CONVERGENCE PROPERTIES

We start with the following lemmas that are essential for the proofs presented in the paper.

For the first term on the right-hand side of (21), we have

which can then be used to obtain

This paper is available on arxiv under CC BY 4.0 DEED license.