Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized
problem-solving technique that allows users to train using massive data. Unprocessed …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Federated learning enables machine learning models to learn from private decentralized
data without compromising privacy. The standard formulation of federated learning produces …

Federated learning (FL) enables participants to collaboratively construct a global machine
learning model without sharing their local training data to the remote server. In FL systems …

Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive
applications without sacrificing the sensitive private information of clients. However, the data …

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized
devices with labeled data in a privacy-preserving manner. However, data with noisy labels …

Federated learning has been explored as a promising solution for training machine learning
models at the network edge, without sharing private user data. With limited resources at the …

Federated learning (FL) is a novel distributed learning framework where multiple
participants collaboratively train a global model without sharing any raw data to preserve …

Federated learning allows distributed clients to train a shared machine learning model while
preserving user privacy. In this framework, user devices (ie, clients) perform local iterations …

Federated learning (FL) is a distributed machine learning method that effectively protects
personal data. Many studies on federated learning assumed that all clients have consistent …