Boosting Fairness and Robustness in Over-the-Air Federated Learning
Halil Yigit Oksuz Fabio Molinari Henning Sprekeler Joerg Raisch
Over-the-Air Computation is a beyond-5G communication strategy that has
recently been shown to be useful for the decentralized training of machine
learning models due to its efficiency. In this paper, we propose an
Over-the-Air federated learning algorithm that aims to provide fairness and
robustness through minmax optimization. By using the epigraph form of the
problem at hand, we show that the proposed algorithm converges to the optimal
solution of the minmax problem. Moreover, the proposed approach does not
require reconstructing channel coefficients by complex encoding-decoding
schemes as opposed to state-of-the-art approaches. This improves both
efficiency and privacy.