Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …

Federated learning has received a great deal of research attention recently, with privacy
protection becoming a key factor in the development of artificial intelligence. Federated …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of
the world's web traffic, making a great data source for various machine learning (ML) …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

Federated learning is a decentralized machine learning approach where clients train
models locally and share model updates to develop a global model. This enables low …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

Federated learning (FL) is a general principle for decentralized clients to train a server
model collectively without sharing local data. FL is a promising framework with practical …