Federated Learning (FL) offers a novel distributed machine learning context whereby a
global model is collaboratively learned through edge devices without violating data privacy …

In recent years, wireless federated learning (FL) has been proposed to support the mobile
intelligent applications over the wireless network, which protects the data privacy and …

Personalized federated learning (PFL) aims to train model (s) that can perform well on the
individual edge-devices' data where the edge-devices (clients) are usually IoT devices like …

As an alternative centralized systems, which may prevent data to be stored in a central
repository due to its privacy and/or abundance, federated learning (FL) is nowadays a game …

Federated Learning (FL) enables the multiple participating devices to collaboratively
contribute to a global neural network model while keeping the training data locally. Unlike …

To cope with the high communication overhead caused by frequent aggregation of
Federated Learning (FL) in Multi-access Edge Computing (MEC) scenarios, Hierarchical …

Personalization in federated learning (FL) functions as a coordinator for clients with high
variance in data or behavior. Ensuring the convergence of these clients' models relies on …

Federated learning (FL) has become increasingly popular and distributes machine learning
models among a large set of resource-constraint edge devices without transferring data to …

Federated learning (FL) is a distributed machine learning paradigm that enables a large
number of clients to collaboratively train models without sharing data. However, when the …

Federated learning is a distributed machine learning method for learning consumer data
generated by consumer electronic devices. It provides personalized intelligent services for …