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Found 12 papers, 0 papers with code
Clipped SGD Algorithms for Privacy Preserving Performative Prediction: Bias Amplification and Remedies
This paper studies the convergence properties of these algorithms in a performative prediction setting, where the data distribution may shift due to the deployed prediction model.
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server.
Multi-Armed Bandits with Self-Information Rewards
Additionally, under the assumption that the \textit{exact} alphabet size is unknown, and instead the player only knows a loose upper bound on it, a UCB-based algorithm is proposed, in which the player aims to reduce the regret caused by the unknown alphabet size in a finite time regime.
Semi-Decentralized Federated Learning with Collaborative Relaying
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS).
Restless Multi-Armed Bandits under Exogenous Global Markov Process
Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time t the arm that maximizes the expected immediate value.
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying
Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks.
Learning in Restless Bandits under Exogenous Global Markov Process
Motivated by recent studies on related RMAB settings, the regret is defined as the reward loss with respect to a player that knows the dynamics of the problem, and plays at each time $t$ the arm that maximizes the expected immediate value.
Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks
The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center.
Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems
Further, the expected convergence rate decays exponentially with the quality of the trust observations between agents.
Optimization and Control Robotics Systems and Control Signal Processing Systems and Control
Interference Reduction in Virtual Cell Optimization
Our numerical results show that our scheme decreases the number of users in the system whose rate falls below the guaranteed rate, set to $128$kbps, $256$kbps or $512$kbps, when compared with our previously proposed optimization methods.
Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks
We refer to this hybrid communication scheme as a cloud-cluster architecture.
The Restless Hidden Markov Bandit with Linear Rewards and Side Information
Furthermore, we assume structural side information where the decision maker knows in advance that there are two types of hidden states; one is common to all arms and evolves according to a Markovian distribution, and the other is unique to each arm and is distributed according to an i. i. d.
