Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

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 …

The Internet of Things (IoT) will be ripe for the deployment of novel machine learning
algorithm for both network and application management. However, given the presence of …

Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …

Federated Learning (FL) enables learning a shared model acrossmany clients without
violating the privacy requirements. One of the key attributes in FL is the heterogeneity that …

Abstract Personalized Federated Learning (pFL), which utilizes and deploys distinct local
models, has gained increasing attention in recent years due to its success in handling the …

The ubiquity of devices in Internet of Things (IoT) has opened up a large source for IoT data.
Machine learning (ML) models with big IoT data is beneficial to our daily life in monitoring air …

Federated Learning (FL) is an approach to conduct machine learning without centralizing
training data in a single place, for reasons of privacy, confidentiality or data volume …

Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …