Federated learning (FL) is a machine learning paradigm for data silos and privacy
protection, which aims to organize multiple clients for training global machine learning …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Federated learning (FL), as a privacy-enhanced distributed machine learning paradigm, has
achieved tremendous success in solving the data silo problem. However, data heterogeneity …

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine
learning models with distributed data stored in different silos without exposing sensitive …

Federated learning (FL), as a machine learning framework, has garnered substantial
attention from researchers in recent years. FL makes it possible to train a global model …

Federated learning (FL) is an important paradigm for training global models from
decentralized data in a privacy-preserving way. Existing FL methods usually assume the …

In contemporary times, artificial intelligence is extensively applied across domains,
concurrently raising concerns about privacy breaches. In response, federated learning has …

Federated Learning (FL) has recently made significant progress as a new machine learning
paradigm for privacy protection. Due to the high communication cost of traditional FL, one …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Federated Learning (FL) enables a large number of data owners (aka FL clients) to jointly
train a machine learning model without disclosing private local data. The importance of local …