The demand for machine learning (ML) has increased significantly in recent decades,
enabling several applications, such as speech recognition, computer vision, and …

When classifying network traffic, a key challenge is deciding when to perform the
classification, ie, after how many packets. Too early, and the decision basis is too thin to …

To keep pace with the explosive growth of Artificial Intelligence (AI) and Machine Learning
(ML)-dominated applications, distributed intelligence solutions are gaining momentum …

While the concept of AI4Net has been widely discussed in the past decade and adopted in
5G, its counterpart, Net4AI, has not gained that much attention so far. This is mostly due to …

Software-Defined Networking (SDN) offers significant advantages for modern networks,
including flexibility, centralized control, and reduced dependency on vendor-specific …

For the future cyber-physical system (CPS) society, it is necessary to construct digital twins
(DTs) of a real world in real time using a lot of cameras and sensors. Hence, the energy …

In existing mobile network systems, the data plane (DP) is mainly considered a pipeline
consisting of network elements end-to-end forwarding user data traffics. With the rapid …

Achieving high-performance aggregation is essential to scale data-parallel distributed
machine learning (ML) training. Recent research efforts in the area of in-network computing …

Low-latency inference for machine learning models is increasingly becoming a necessary
requirement, as these models are used in mission-critical applications such as autonomous …

The problem of attacks on new generation network infrastructures is becoming increasingly
relevant, given the widening of the attack surface of these networks resulting from the …