Client-wise data heterogeneity is one of the major issues that hinder effective training in
federated learning (FL). Since the data distribution on each client may vary dramatically, the …

Federated Learning (FL) is a distributed learning paradigm that enables a large number of
resource-limited nodes to collaboratively train a model without data sharing. The non …

In Federated Learning (FL), a global statistical model is developed by encouraging mobile
users to perform the model training on their local data and aggregating the output local …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …

Federated learning (FL) has been widely acknowledged as a promising solution to training
machine learning (ML) model training with privacy preservation. To reduce the traffic …

Federated learning is an emerging concept that trains the machine learning models with the
local distributed data sets, without sending the raw data to the data center. But, in the …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Federated learning is promising in enabling large-scale machine learning by massive
mobile devices without exposing the raw data of users with strong privacy concerns. Existing …

Federated learning enables distributed model training over various computing nodes, eg,
mobile devices, where instead of sharing raw user data, computing nodes can solely commit …