In order to meet the extremely heterogeneous requirements of the next generation wireless
communication networks, research community is increasingly dependent on using machine …

Recent advancements in deep learning methods bring computer-assistance a step closer to
fulfilling promises of safer surgical procedures. However, the generalizability of such …

Federated Learning (FL) trains a machine learning model on distributed clients without
exposing individual data. Unlike centralized training that is usually based on carefully …

Supervised federated learning (FL) enables multiple clients to share the trained model
without sharing their labeled data. However, potential clients might even be reluctant to label …

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) is a promising paradigm for future sixth-generation wireless systems
to underpin network edge intelligence for smart cities applications. However, most of the …

Deploying machine learning applications on edge devices can bring clear benefits such as
improved reliability, latency and privacy but it also introduces its own set of challenges. Most …

Recently, federated learning (FL) has received intensive research because of its ability in
preserving data privacy for scattered clients to collaboratively train machine learning …

Federated learning (FL) is a machine learning paradigm that enables a cluster of
decentralized edge devices to collaboratively train a shared machine learning model without …

In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and
computation-capable nodes are available. Such nodes can cooperate to perform a …