Boosting Fairness and Robustness in Over-the-Air Federated Learning: FedAir Algorithm

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

III. FEDERATED FAIR OVER-THE-AIR LEARNING (FEDFAIR) ALGORITHM

Next, we state our assumptions on individual objective functions and the step size as follows:

We refer here to [28, Ch 2.3, Ch 2.4] and [29, Ch 5.4], thus considering channel coefficients independent realizations (see [30]).

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