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

Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively
train a central model while the training data remains decentralized. Existing FL methods …

Federated learning enables the creation of a powerful centralized model without
compromising data privacy of multiple participants. While successful, it does not incorporate …

Federated Learning (FL) enables the training of Deep Learning models without centrally
collecting possibly sensitive raw data. This paves the way for stronger privacy guarantees …

Conventional federated learning directly averages model weights, which is only possible for
collaboration between models with homogeneous architectures. Sharing prediction instead …

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

Federated learning (FL) is a privacy-preserving distributed learning approach that allows
multiple parties to jointly build machine learning models without disclosing sensitive data …

State-of-the-art edge devices are capable of not only inferring machine learning (ML)
models but also training them on the device with local data. When this local data is sensitive …

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 …