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 …
growth has been fueled by advances in machine learning techniques and the ability to …
Edge intelligence: Architectures, challenges, and applications
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 …
caching, processing, and analysis in locations close to where data is captured based on …
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 …
Edge intelligence: Empowering intelligence to the edge of network
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 …
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
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 …
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
Data science projects represent a greater challenge than software engineering for
organizations pursuing their adoption. The diverse stakeholders involved emphasize the …
organizations pursuing their adoption. The diverse stakeholders involved emphasize the …
Revisiting edge ai: Opportunities and challenges
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 …
training and inference of machine learning models to the edge of the network. This paradigm …
Learning robots to grasp by demonstration
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 …
cobots, that are designed to work safely along with human operators. These cobots typically …
Tinymlops: Operational challenges for widespread edge ai adoption
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 …
improved reliability, latency and privacy but it also introduces its own set of challenges. Most …
[PDF][PDF] A survey on edge intelligence
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 …
caching, processing, and analysis in locations close to where data is captured based on …