Pervasive AI for IoT applications: A survey on resource-efficient distributed artificial intelligence
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …
Things (IoT) applications and services, spanning from recommendation systems and speech …
Exploiting heterogeneity in robust federated best-arm identification
On-demand communication for asynchronous multi-agent bandits
This paper studies a cooperative multi-agent multi-armed stochastic bandit problem where
agents operate asynchronously–agent pull times and rates are unknown, irregular, and …
agents operate asynchronously–agent pull times and rates are unknown, irregular, and …
Differentially private linear bandits with partial distributed feedback
In this paper, we study the problem of global reward maximization with only partial
distributed feedback. This problem is motivated by several real-world applications (eg …
distributed feedback. This problem is motivated by several real-world applications (eg …
Reinforcement learning for user association and handover in mmwave-enabled networks
Using a multi-armed bandit technique, we propose centralized and semi-distributed online
algorithms for load balancing user association and handover in mmWave-enabled networks …
algorithms for load balancing user association and handover in mmWave-enabled networks …
Multi-agent best arm identification with private communications
We address multi-agent best arm identification with privacy guarantees. In this setting,
agents collaborate by communicating to find the optimal arm. To avoid leaking sensitive data …
agents collaborate by communicating to find the optimal arm. To avoid leaking sensitive data …
Decentralized randomly distributed multi-agent multi-armed bandit with heterogeneous rewards
We study a decentralized multi-agent multi-armed bandit problem in which multiple clients
are connected by time dependent random graphs provided by an environment. The reward …
are connected by time dependent random graphs provided by an environment. The reward …
Collaborative linear bandits with adversarial agents: Near-optimal regret bounds
We consider a linear stochastic bandit problem involving $ M $ agents that can collaborate
via a central server to minimize regret. A fraction $\alpha $ of these agents are adversarial …
via a central server to minimize regret. A fraction $\alpha $ of these agents are adversarial …