… the local training at the client, or improvements to the global aggregation process at the server.
In this work, we focus on local training … As we focus on the local training, these works are …

… proach for training ML … federated learning (FL) from the local perspective of individual
participants and investigate whether they have an incentive to participate. Does federated learning …

… Federated learning is a method of training models on private data distributed over multiple
… To this end, we propose a new federated learning algorithm that jointly learns compact local …

… In this work, we propose combining federated training of global parameters with … fraction of
clients may be sampled for training. To motivate partially local federated learning, we begin by …

… Motivated by learning a centralized global model from training data distributed over … sharing
local data, the Federated Learning (FL) is pioneered as a special case of distributed learning …

… We propose a new optimization formulation for training federated learning models. The
standard formulation has the form of an empirical risk minimization problem constructed to find a …

… of federated learning. Finally, we summarize the characteristics of existing federated learning,
… data protection in machine learning, we must ensure that the training model in federated …

… tations to correct the local training of individual parties, ie, conducting contrastive … local
models for each party. In this paper, we study the typical federated learning, which tries to learn …

… Distributed machine learning in the form of Federated Learning (FL) has been applied to …
In this prospect, we present a Federated Learning implementation based on a neural network …

… These studies on federated learning optimization algorithms focus on accelerating the
convergence time of global models by improving the training efficiency of local models and …