Recently, graph neural networks (GNNs) have gained much attention as a
g...
Federated Machine Learning (FL) has received considerable attention in r...
Training deep learning models in the cloud or on dedicated hardware is
e...
Graph Neural Networks (GNNs) are an emerging research field. This specia...
The aim of dataset distillation is to encode the rich features of an ori...
Dataset distillation is attracting more attention in machine learning as...
For distributed graph processing on massive graphs, a graph is partition...
Aside from the conception of new blockchain architectures, existing
bloc...
Existing permissioned blockchains often rely on coordination-based conse...
The DEBS Grand Challenge (GC) is an annual programming competition open ...
Graph edge partitioning is an important preprocessing step to optimize
d...
Preprocessing pipelines in deep learning aim to provide sufficient data
...
Distributed systems that manage and process graph-structured data intern...
Permissioned blockchain systems promise to provide both decentralized tr...
Graph partitioning is an important preprocessing step to distributed gra...
Deep Learning (DL) has had an immense success in the recent past, leadin...
Stream Processing (SP) has evolved as the leading paradigm to process an...
Many important real-world applications-such as social networks or distri...
In-network computing using programmable networking hardware is a strong ...
Arising user-centric graph applications such as route planning and
perso...
In recent years, the graph partitioning problem gained importance as a
m...
State-of-the-art data flow systems such as TensorFlow impose iterative
c...
With the advent of the Internet of Things and Industry 4.0 an enormous a...