Federated Learning (FL) is a novel distributed machine learning which allows thousands of
edge devices to train model locally without uploading data concentrically to the server. But …

Federated Learning (FL) is a novel distributed machine learning, which allows thousands of
edge devices to train models locally without uploading data to the central server. Since …

Federated learning (FL) is a novel machine learning that performs distributed training locally
on devices and aggregating the local models into a global one. The limited network …

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

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

Federated learning (FL) supports training models on geographically distributed devices.
However, traditional FL systems adopt a centralized synchronous strategy, putting high …

Federated learning (FL) has been widely recognized as a promising approach by enabling
individual end-devices to cooperatively train a global model without exposing their own …

Driven by emerging technologies such as edge computing and Internet of Things (IoT),
recent years have witnessed the increasing growth of data processing in a distributed way …

Federated learning (FL) has attracted increasing attention as a promising technique to drive
a vast number of edge devices with artificial intelligence. However, it is very challenging to …

Federated learning (FL) becomes a promising machine learning paradigm. The impact of
heterogeneous hardware specifications and dynamic states on the FL process has not yet …