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) …

Federated learning (FL) and split learning (SL) are two popular distributed machine learning
approaches. Both follow a model-to-data scenario; clients train and test machine learning …

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 …

Applying federated learning (FL) on Internet of Things (IoT) devices is necessitated by the
large volumes of data they produce and growing concerns of data privacy. However, there …

Recently, edge AI has been launched to mine and discover valuable knowledge at network
edge. Federated Learning, as an emerging technique for edge AI, has been widely …

Federated learning (FL) has gained increasing attention due to its ability to collaboratively
train while protecting client data privacy. However, vanilla FL cannot adapt to client …

Abstract We propose Compressed Vertical Federated Learning (C-VFL) for communication-
efficient training on vertically partitioned data. In C-VFL, a server and multiple parties …

Split learning (SL) is a promising distributed learning framework that enables to utilize the
huge data and parallel computing resources of mobile devices. SL is built upon a model …

Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine
valuable knowledge in edge computing (EC) systems. To boost the performance of AI …

Federated learning enables collaborative training of a model while keeping the training data
decentralized and private. However, in IoT systems, inherent heterogeneity in processing …