Federated learning aims to train a machine learning model (eg, a neural network) in a data-
decentralized fashion. The key challenge is the potential data heterogeneity among clients …

Federated learning data is drawn from a distribution of distributions: clients are drawn from a
meta-distribution, and their data are drawn from local data distributions. Thus generalization …

Model averaging is a widely adopted technique in federated learning (FL) that aggregates
multiple client models to obtain a global model. Remarkably, model averaging in FL yields a …

Federated learning are inherently hampered by data heterogeneity: non-iid distributed
training data over local clients. We propose a novel model training approach for federated …

Federated learning methods typically learn a model by iteratively sampling updates from a
population of clients. In this work, we explore how the number of clients sampled at each …

Federated learning typically considers collaboratively training a global model using local
data at edge clients. Clients may have their own individual requirements, such as having a …

Federated learning (FL) enables multiple clients to train a model while keeping their data
private collaboratively. Previous studies have shown that data heterogeneity between clients …

Federated learning aims to train models collaboratively across different clients without
sharing data for privacy considerations. However, one major challenge for this learning …

Federated Learning (FL) has emerged as a means of distributed learning using local data
stored at clients with a coordinating server. Recent studies showed that FL can suffer from …

Federated learning (FL) is a distributed machine learning technique in which multiple clients
cooperate to train a shared model without exchanging their raw data. However …