[HTML][HTML] Combined federated and split learning in edge computing for ubiquitous intelligence in internet of things: State-of-the-art and future directions
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) …
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …
Splitfed: When federated learning meets split learning
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 …
approaches. Both follow a model-to-data scenario; clients train and test machine learning …
Decentralized learning in healthcare: a review of emerging techniques
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 …
healthcare. The performance of a deep learning model generally improves with the size of …
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
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 …
large volumes of data they produce and growing concerns of data privacy. However, there …
Accelerating federated learning with data and model parallelism in edge computing
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 …
edge. Federated Learning, as an emerging technique for edge AI, has been widely …
Compressed-vfl: Communication-efficient learning with vertically partitioned data
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 …
efficient training on vertically partitioned data. In C-VFL, a server and multiple parties …
Ringsfl: An adaptive split federated learning towards taming client heterogeneity
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 …
train while protecting client data privacy. However, vanilla FL cannot adapt to client …
Locfedmix-sl: Localize, federate, and mix for improved scalability, convergence, and latency in split learning
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 …
huge data and parallel computing resources of mobile devices. SL is built upon a model …
SplitGP: Achieving both generalization and personalization in federated learning
A fundamental challenge to providing edge-AI services is the need for a machine learning
(ML) model that achieves personalization (ie, to individual clients) and generalization (ie, to …
(ML) model that achieves personalization (ie, to individual clients) and generalization (ie, to …
Federated Split Learning With Joint Personalization-Generalization for Inference-Stage Optimization in Wireless Edge Networks
The demand for intelligent services at the network edge has introduced several research
challenges. One is the need for a machine learning architecture that achieves …
challenges. One is the need for a machine learning architecture that achieves …