3 accepted papers
Network traffic forecasting plays a crucial role in intelligent network operations, but existing techniques often perform poorly when faced with limited data. Additionally, multi-task learning methods struggle with task imbalance and negative transfer, especially when modeling various service types.…
Model stealing (MS) involves querying and observing the output of a machine learning model to steal its capabilities. The quality of queried data is crucial yet obtaining a large amount of real data for MS is often challenging. Recent works have reduced reliance on real data by using generative mode…
Graph Neural Networks (GNNs) as an emerging technique have shown excellent performance in a variety of fields, such as social networks and recommendation systems. However, GNNs may have to overcome privacy concerns as large amounts of information about their training datasets may be compromised. In…