Data sharing between different parties has become increasingly common ac...
Prior efforts have shown that network-assisted schemes can improve the
Q...
Foundational models have caused a paradigm shift in the way artificial
i...
Performance issues in software-defined network (SDN) controllers can hav...
Today's large-scale services (e.g., video streaming platforms, data cent...
The privacy implications of generative adversarial networks (GANs) are a...
We study the problem of learning generative adversarial networks (GANs) ...
As the vision of in-network computing becomes more mature, we see two
pa...
Generative adversarial networks (GANs) are often billed as "universal
di...
Sketching algorithms or sketches have emerged as a promising alternative...
Spectral normalization (SN) is a widely-used technique for improving the...
Many recent efforts have shown that in-network computing can benefit var...
The DDoS attack landscape is growing at an unprecedented pace. Inspired ...
In response to growing concerns about user privacy, federated learning h...
Communication and privacy are two critical concerns in distributed learn...
Limited data access is a substantial barrier to data-driven networking
r...
In light of ever-increasing scale and sophistication of modern DDoS atta...
The recent October 2016 DDoS attack on Dyn served as a wakeup call to th...
Modern automobiles are entirely controlled by electronic circuits and
pr...