Federated Learning allows the training of machine learning models by using the
computation and private data resources of many distributed clients. Most existing results on …

Federated learning allows multiple clients to jointly train a model on their private data
without revealing their local data to a centralized server. Thereby, federated learning has …

Air access networks have been recognized as a significant driver of various Internet of
Things (IoT) services and applications. In particular, the aerial computing network …

Federated Learning allows training machine learning models by using the computation and
private data resources of a large number of distributed clients such as smartphones and IoT …

Speech Emotion Recognition (SER) refers to the recognition of human emotions from
natural speech. If done accurately, it can offer a number of benefits in building human …

Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-
the-cloud setting to distributed training over edge devices in order to strengthen data …

To facilitate the implementation of deep learning-based vehicular applications, vehicular
federated learning is introduced by integrating vehicular edge computing with the newly …

Federated learning (FL) has been widely applied to collaboratively train deep learning (DL)
models on massive end devices (ie, clients). Due to the limited storage capacity and high …

Federated Learning (FL) allows edge devices (or clients) to keep data locally while
simultaneously training a shared high-quality global model. However, current research is …

Federated learning (FL) has been widely applied to collaboratively train deep learning (DL)
models on massive end devices (ie, clients). Due to the limited storage capacity and high …