The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
Computing graph neural networks: A survey from algorithms to accelerators
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …
years owing to their capability to model and learn from graph-structured data. Such an ability …
Gossipfl: A decentralized federated learning framework with sparsified and adaptive communication
Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …
learning models collaboratively without data sharing. However, existing FL algorithms suffer …
Chimera: efficiently training large-scale neural networks with bidirectional pipelines
Training large deep learning models at scale is very challenging. This paper proposes
Chimera, a novel pipeline parallelism scheme which combines bidirectional pipelines for …
Chimera, a novel pipeline parallelism scheme which combines bidirectional pipelines for …
[HTML][HTML] Privacy and security in federated learning: A survey
In recent years, privacy concerns have become a serious issue for companies wishing to
protect economic models and comply with end-user expectations. In the same vein, some …
protect economic models and comply with end-user expectations. In the same vein, some …
A survey of on-device machine learning: An algorithms and learning theory perspective
The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …
model in the cloud and perform inference using the trained model on the device. However …
[HTML][HTML] Distributed artificial intelligence-as-a-service (DAIaaS) for smarter IoE and 6G environments
Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything
we do, even in finding our “true love” and the “significant other”. While 5G promises us high …
we do, even in finding our “true love” and the “significant other”. While 5G promises us high …
HSIC bottleneck based distributed deep learning model for load forecasting in smart grid with a comprehensive survey
Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
Federated learning in robotic and autonomous systems
Autonomous systems are becoming inherently ubiquitous with the advancements of
computing and communication solutions enabling low-latency offloading and real-time …
computing and communication solutions enabling low-latency offloading and real-time …
Distributed robotic systems in the edge-cloud continuum with ros 2: A review on novel architectures and technology readiness
Robotic systems are more connected, networked, and distributed than ever. New
architectures that comply with the de facto robotics middleware standard, ROS 2, have …
architectures that comply with the de facto robotics middleware standard, ROS 2, have …