Boosting Fairness and Robustness in Over-the-Air Federated Learning

arXiv:2403.04431v1 Announce Type: new
Abstract: 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 …

abstract algorithm arxiv beyond boosting communication computation cs.cy cs.lg decentralized efficiency fairness federated learning machine machine learning machine learning models optimization paper robustness strategy through training type