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 …

Federated learning (FL) enables multiple clients to jointly train a global model under the
coordination of a central server. Although FL is a privacy-aware paradigm, where raw data …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Federated learning (FL) is an attractive paradigm for privacy-aware distributed machine
learning, which enables clients to collaboratively learn a global model without sharing …

Federated learning provides a communication-efficient and privacy-preserving training
process by enabling learning statistical models with massive participants without accessing …

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 …

Learning a global model by abstracting the knowledge, distributed across multiple clients,
without aggregating the raw data is the primary goal of Federated Learning (FL). Typically …

Federated Learning (FL) has emerged as an effective paradigm for distributed learning
systems owing to its strong potential in exploiting underlying data characteristics while …

Federated learning (FL) enables leveraging distributed private data for model training in a
privacy-preserving way. However, data heterogeneity significantly limits the performance of …

Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed
clients to collaboratively train models with a central server while keeping raw data locally. In …