Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …

Distributed online optimization and online games have been increasingly researched in the
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …

While training a machine learning model using multiple workers, each of which collects data
from its own data source, it would be useful when the data collected from different workers …

Multi-agent systems can solve scientific issues related to complex systems that are difficult or
impossible for a single agent to solve through mutual collaboration and cooperation …

Existing approaches to online convex optimization make sequential one-slot-ahead
decisions, which lead to (possibly adversarial) losses that drive subsequent decision …

This paper considers distributed online optimization with time-varying coupled inequality
constraints. The global objective function is composed of local convex cost and …

Motivated by emerging need of learning algorithms for large scale networked and
decentralized systems, we introduce a distributed version of the classical stochastic Multi …

We consider the problem of multiagent optimization wherein an unknown subset of agents
suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a …

Federated learning has become increasingly important for modern machine learning,
especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the …

Distributed learning has become a critical enabler of the massively connected world that
many people envision. This article discusses four key elements of scalable distributed …