Boosting Fairness and Robustness in Over-the-Air Federated Learning: Problem Setup

by Computational Technology for AllOctober 27th, 2024

Too Long; Didn't Read

This paper presents a federated learning algorithm using Over-the-Air computation for fairness and robustness, optimizing performance in decentralized networks.

featured image - Boosting Fairness and Robustness in Over-the-Air Federated Learning: Problem Setup

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

II. PROBLEM SETUP

A. Minmax Reformulation

In a federated learning setting with N agents, where V = {1,2,··· ,N} denotes the index set, we are interested in improving the performance of the worst-performing agent by solving the following optimization problem:

We aim to compute a parameter vector estimate minimizing the worst-case loss observed among all agents, thus providing some form of fairness [20], [21]. However, it is difficult and inefficient to use (3) directly for federated learning purposes. Instead, we can consider an alternative (epigraph) form: