Federated learning (FL) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

Abstract Model heterogeneous federated learning is a challenging task since each client
independently designs its own model. Due to the annotation difficulty and free-riding …

Federated learning (FL) is a distributed machine learning technique that enables
collaborative model training while avoiding explicit data sharing. The inherent privacy …

Recent developments in deep learning have contributed to numerous success stories in
healthcare. The performance of a deep learning model generally improves with the size of …

As edge computing faces increasingly severe data security and privacy issues of edge
devices, a framework called federated edge learning (FEL) has recently been proposed to …

Limited compute, memory, and communication capabilities of edge users create a significant
bottleneck for federated learning (FL) of large models. Current literature typically tackles the …

Distributed machine learning methods like Federated Learning (FL) and Split Learning (SL)
meet the growing demands of processing large-scale datasets under privacy restrictions …

To make machine learning (ML) sustainable and apt to run on the diverse devices where
relevant data is, it is essential to compress ML models as needed, while still meeting the …

Edge Computing (EC) has gained significant traction in recent years, promising enhanced
efficiency by integrating Artificial Intelligence (AI) capabilities at the edge. While the focus …