Boosting Fairness and Robustness in Over-the-Air Federated Learning: FedAir Algorithm by@computational
Boosting Fairness and Robustness in Over-the-Air Federated Learning: FedAir Algorithm
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.
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
Federated fair over-the-air learning (FedAir) Algorithm
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.
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