Augment Online Linear Optimization with Arbitrarily Bad Machine-Learned Predictions
The online linear optimization paradigm is important to many real-world network
applications as well as theoretical algorithmic studies. Recent studies have made attempts …
applications as well as theoretical algorithmic studies. Recent studies have made attempts …
Time-Distributed Feature Learning for Internet of Things Network Traffic Classification
Deep learning-based network traffic classification (NTC) techniques, including conventional
and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service …
and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service …
Byzantine-Resilient Online Federated Learning with Applications to Network Traffic Classification
Rapid growth in distributed streaming data at the network edge in many applications has
prompted the emergence of online federated learning (OFL), a promising distributed …
prompted the emergence of online federated learning (OFL), a promising distributed …
Robust Decentralized Online Optimization Against Malicious Agents
Decentralized online optimization, a pivotal paradigm in machine learning, involves multiple
agents making online decisions cooperatively in a decentralized network. Despite its …
agents making online decisions cooperatively in a decentralized network. Despite its …
Augment Decentralized Online Convex Optimization with Arbitrarily Bad Machine-Learned Predictions
Decentralized online convex optimization (DOCO), as a pivotal computational paradigm in
machine learning, has been applied to many critical tasks. However, existing DOCO …
machine learning, has been applied to many critical tasks. However, existing DOCO …