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) offers a solution to train a global machine learning model while still
maintaining data privacy, without needing access to data stored locally at the clients …

Federated learning (FL) has enabled training machine learning models exploiting the data
of multiple agents without compromising privacy. However, FL is known to be vulnerable to …

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 …

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) is a privacy-preserving distributed machine learning paradigm.
Nonetheless, the substantial distribution shifts among clients pose a considerable challenge …

Federated learning is a widely used distributed deep learning framework that protects the
privacy of each client by exchanging model parameters rather than raw data. However …

Federated Learning (FL) is a promising paradigm that offers significant advancements in
privacy-preserving, decentralized machine learning by enabling collaborative training of …

Federated learning (FL) is a distributed machine learning approach involving multiple clients
collaboratively training a shared model. Such a system has the advantage of more training …

Federated Learning (FL) is a distributed machine learning approach that enables training on
decentralized data while preserving privacy. However, FL systems often involve resource …