Federated learning is considered as a privacy-preserving distributed machine learning
framework, where the model training is distributed over end devices by fully exploiting …

Federated Learning (FL) is an emerging technique to enhance edge intelligence, where
mobile devices train machine learning models collaboratively with their local data. Limited …

Federated learning (FL) is recognized as a promising privacy-preserving distributed
machine learning paradigm, given its potential to enable collaborative model training among …

Federated Learning is designed for multiple mobile devices to collaboratively train an
artificial intelligence model while preserving data privacy. Instead of collecting the raw …

With the explosive growth of massive data generated by smart Internet of Things (IoT)
devices, federated learning has been envisioned as a promising technique to provide …

Federated learning (FL) has become a promising solution to train a shared model without
exchanging local training samples. However, in the traditional cloud-based FL framework …

Potential environmental impact of machine learning in large-scale wireless networks is a
major challenge for the sustainability of next-generation intelligent systems. Federated …

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 (FL) is a distributed machine learning technique to address the data
privacy issue. Participant selection is critical to determine the latency of the training process …

Federated learning enables distributed agents to train a common and shared model in a
privacy-protected way. Due to the heterogeneity of computing and communication …