[HTML][HTML] A review on TinyML: State-of-the-art and prospects
PP Ray - Journal of King Saud University-Computer and …, 2022 - Elsevier
Abstract Machine learning has become an indispensable part of the existing technological
domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to …
domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to …
Machine learning for microcontroller-class hardware: A review
The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …
[HTML][HTML] Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges
Abstract The application of Machine Learning (ML) techniques to the well-known intrusion
detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks …
detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks …
Federated learning for edge computing: A survey
New technologies bring opportunities to deploy AI and machine learning to the edge of the
network, allowing edge devices to train simple models that can then be deployed in practice …
network, allowing edge devices to train simple models that can then be deployed in practice …
Breaking physical and linguistic borders: Multilingual federated prompt tuning for low-resource languages
Pretrained large language models (LLMs) have emerged as a cornerstone in modern
natural language processing, with their utility expanding to various applications and …
natural language processing, with their utility expanding to various applications and …
On-device training of machine learning models on microcontrollers with federated learning
N Llisterri Giménez, M Monfort Grau… - Electronics, 2022 - mdpi.com
Recent progress in machine learning frameworks has made it possible to now perform
inference with models using cheap, tiny microcontrollers. Training of machine learning …
inference with models using cheap, tiny microcontrollers. Training of machine learning …
Tiny Machine Learning for Resource‐Constrained Microcontrollers
R Immonen, T Hämäläinen - Journal of Sensors, 2022 - Wiley Online Library
We use 250 billion microcontrollers daily in electronic devices that are capable of running
machine learning models inside them. Unfortunately, most of these microcontrollers are …
machine learning models inside them. Unfortunately, most of these microcontrollers are …
Reliable federated learning in a cloud-fog-IoT environment
The paper presents RelFL, a Rel iable F ederated L earning system for collaborative and
decentralized training of a deep learning model in a cloud-fog-Internet of Things (IoT) …
decentralized training of a deep learning model in a cloud-fog-Internet of Things (IoT) …
Intrusion Detection based on Federated Learning: a systematic review
The evolution of cybersecurity is undoubtedly associated and intertwined with the
development and improvement of artificial intelligence (AI). As a key tool for realizing more …
development and improvement of artificial intelligence (AI). As a key tool for realizing more …
[HTML][HTML] Embedded federated learning over a LoRa mesh network
In on-device training of machine learning models on microcontrollers a neural network is
trained on the device. A specific approach for collaborative on-device training is federated …
trained on the device. A specific approach for collaborative on-device training is federated …