Abstract Machine learning over distributed data stored by many clients has important
applications in use cases where data privacy is a key concern or central data storage is not …

Federated learning is a distributed machine learning approach for computing models over
data collected by edge devices. Most importantly, the data itself is not collected centrally, but …

The emerging concern about data privacy and security has motivated the proposal of
federated learning, which allows nodes to only synchronize the locally-trained models …

Machine learning over fully distributed data poses an important problem in peer‐to‐peer
applications. In this model, we have one data record at each network node but without the …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

The emerging concern about data privacy and security has motivated the proposal of
federated learning. Federated learning allows computing nodes to only synchronize the …

Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …

Federated learning is a distributed framework according to which a model is trained over a
set of devices, while keeping data localized. This framework faces several systems-oriented …

Communication overhead hinders the scalability of large-scale distributed training. Gossip
SGD, where each node averages only with its neighbors, is more communication-efficient …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …