A survey on distributed machine learning

J Verbraeken, M Wolting, J Katzy… - Acm computing surveys …, 2020 - dl.acm.org
The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

Edge intelligence: Architectures, challenges, and applications

D Xu, T Li, Y Li, X Su, S Tarkoma, T Jiang… - arXiv preprint arXiv …, 2020 - arxiv.org
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …

Splitfed: When federated learning meets split learning

C Thapa, PCM Arachchige, S Camtepe… - Proceedings of the AAAI …, 2022 - ojs.aaai.org
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 …

Edge intelligence: Empowering intelligence to the edge of network

D Xu, T Li, Y Li, X Su, S Tarkoma, T Jiang… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

An artificial intelligence-based collaboration approach in industrial iot manufacturing: Key concepts, architectural extensions and potential applications

P Trakadas, P Simoens, P Gkonis, L Sarakis… - Sensors, 2020 - mdpi.com
The digitization of manufacturing industry has led to leaner and more efficient production,
under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and …

A joint study of the challenges, opportunities, and roadmap of mlops and aiops: A systematic survey

J Diaz-De-Arcaya, AI Torre-Bastida, G Zárate… - ACM Computing …, 2023 - dl.acm.org
Data science projects represent a greater challenge than software engineering for
organizations pursuing their adoption. The diverse stakeholders involved emphasize the …

Revisiting edge ai: Opportunities and challenges

T Meuser, L Lovén, M Bhuyan, SG Patil… - IEEE Internet …, 2024 - ieeexplore.ieee.org
Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the
training and inference of machine learning models to the edge of the network. This paradigm …

Learning robots to grasp by demonstration

E De Coninck, T Verbelen, P Van Molle… - Robotics and …, 2020 - Elsevier
In recent years, we have witnessed the proliferation of so-called collaborative robots or
cobots, that are designed to work safely along with human operators. These cobots typically …

Tinymlops: Operational challenges for widespread edge ai adoption

S Leroux, P Simoens, M Lootus… - 2022 IEEE …, 2022 - ieeexplore.ieee.org
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 …

[PDF][PDF] A survey on edge intelligence

D Xu, T Li, Y Li, X Su, S Tarkoma… - arXiv preprint arXiv …, 2020 - academia.edu
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …