Boosting Fairness and Robustness in Over-the-Air Federated Learning: Convergence Properties by@computational
Boosting Fairness and Robustness in Over-the-Air Federated Learning: Convergence Properties
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
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.
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