Federated Learning (FL) is a powerful technique to train a model on a server with data from
several clients in a privacy-preserving manner. FL incurs significant communication costs …

Federated learning (FL) ameliorates privacy concerns in settings where a central server
coordinates learning from data distributed across many clients. The clients train locally and …

Federated learning allows multiple parties to jointly train a deep learning model on their
combined data, without any of the participants having to reveal their local data to a …

Learning over massive data stored in different locations is essential in many real-world
applications. However, sharing data is full of challenges due to the increasing demands of …

Federated Learning (FL) has recently received a lot of attention for large-scale privacy-
preserving machine learning. However, high communication overheads due to frequent …

Federated learning is a privacy-preserving machine learning technique to train intelligent
models from decentralized data, which enables exploiting private data by communicating …

Model compression is important in federated learning (FL) with large models to reduce
communication cost. Prior works have been focusing on sparsification based compression …

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) enables distributed optimization of machine learning models while
protecting privacy by independently training local models on each client and then …

Federated learning (FL) is an emerging paradigm for training machine learning models
using possibly private data available at edge devices. The distributed operation of FL gives …