Federated learning (FL) facilitates collaboration between a group of clients who seek to train
a common machine learning model without directly sharing their local data. Although there …

In federated learning (FL), clients collectively train a global machine learning model with
their own local data. Without sharing sensitive raw data, each client in FL only sends …

Federated learning is a promising approach for collaboratively training machine learning
models while keeping the training data decentralized. This paper discusses recent …

Federated Learning (FL) is a decentralized machine learning protocol that allows a set of
participating agents to collaboratively train a model without sharing their data. This makes …

Federated Learning (FL) has gained considerable attention recently, as it allows clients to
cooperatively train a global machine learning model without sharing raw data. However, its …

Federated Learning (FL) is emerging as a popular, promising decentralized learning
framework that enables collaborative training among clients, with no need to share private …

Federated Learning (FL) enables distributed training by learners using local data, thereby
enhancing privacy and reducing communication. However, it presents numerous challenges …

In federated learning (FL), local models are trained independently by clients, local model
parameters are shared with a global aggregator or server, and then the updated model is …

In federated learning (FL), clients collectively train a global machine learning model with
their own local data. Without sharing sensitive raw data, each client in FL only sends …

Federated Learning (FL) is a distributed training paradigm that enables clients scattered
across the world to cooperatively learn a global model without divulging confidential data …