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Distributed optimization for control
Advances in wired and wireless technology have necessitated the development of theory,
models, and tools to cope with the new challenges posed by large-scale control and …
models, and tools to cope with the new challenges posed by large-scale control and …
Peer-to-peer federated learning on graphs
We consider the problem of training a machine learning model over a network of nodes in a
fully decentralized framework. The nodes take a Bayesian-like approach via the introduction …
fully decentralized framework. The nodes take a Bayesian-like approach via the introduction …
[PDF][PDF] Fully decentralized federated learning
We consider the problem of training a machine learning model over a network of users in a
fully decentralized framework. The users take a Bayesian-like approach via the introduction …
fully decentralized framework. The users take a Bayesian-like approach via the introduction …
Fast convergence rates for distributed non-Bayesian learning
We consider the problem of distributed learning, where a network of agents collectively aim
to agree on a hypothesis that best explains a set of distributed observations of conditionally …
to agree on a hypothesis that best explains a set of distributed observations of conditionally …
Geometrically convergent distributed optimization with uncoordinated step-sizes
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have
geometric convergence over time-varying undirected/directed graphs [1]. Nevertheless, an …
geometric convergence over time-varying undirected/directed graphs [1]. Nevertheless, an …
Social learning and distributed hypothesis testing
This paper considers a problem of distributed hypothesis testing over a network. Individual
nodes in a network receive noisy local (private) observations whose distribution is …
nodes in a network receive noisy local (private) observations whose distribution is …
Multi-armed bandits in multi-agent networks
This paper addresses the multi-armed bandit problem in a multi-player framework. Players
explore a finite set of arms with stochastic rewards, and the reward distribution of each arm …
explore a finite set of arms with stochastic rewards, and the reward distribution of each arm …
A tutorial on distributed (non-bayesian) learning: Problem, algorithms and results
We overview some results on distributed learning with focus on a family of recently proposed
algorithms known as non-Bayesian social learning. We consider different approaches to the …
algorithms known as non-Bayesian social learning. We consider different approaches to the …
Convergence rate of distributed averaging dynamics and optimization in networks
Recent advances in wired and wireless technology lead to the emergence of large-scale
networks such as Internet, wireless mobile ad-hoc networks, swarm robotics, smart-grid, and …
networks such as Internet, wireless mobile ad-hoc networks, swarm robotics, smart-grid, and …
Distributed consensus optimization in multiagent networks with time-varying directed topologies and quantized communication
This paper considers solving a class of optimization problems which are modeled as the
sum of all agents' convex cost functions and each agent is only accessible to its individual …
sum of all agents' convex cost functions and each agent is only accessible to its individual …