Traditional federated learning addresses the data security issues arising from the need to
centralize client datasets on a central server for model training. However, this approach still …

Federated learning (FL) is a popular distributed learning framework that can reduce privacy
risks by not explicitly sharing private data. In this work, we explicitly uncover external …

With the rapid development of machine learning and growing concerns about data privacy,
federated learning has become an increasingly prominent focus. However, challenges such …

Federated learning is a machine learning technique that enables training across
decentralized data. Recently, federated learning has become an active area of research due …

Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of
machine learning models on networked devices (eg, mobile devices, IoT edge nodes). It …

In federated learning (FL), multiple clients use local datasets to train models and submit
local gradients to the server for aggregation. However, malicious clients may compromise …

Federated learning (FL) permits a vast number of connected to construct deep learning
models while keeping their private training data on the device. Rather than uploading the …

Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …

Federated learning (FL) is a distributed machine learning framework, which allows multiple
users to collaboratively train and obtain a global model with high accuracy. Currently, FL is …

Federated learning (FL) is a promising technique for collaboratively training machine-
learning models on massively distributed clients data under privacy constraints. However …