Abstract Federated learning (FL) is an attractive distributed machine learning paradigm that enables numerous clients to collaboratively train a model under the coordination of a central server, while keeping the training data private. However, without sufficient incentive, clients may be reluctant to participate in FL due to the associated training costs and variant availability. Moreover, […]
