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Found 22 papers, 0 papers with code
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
We consider a setting in which $N$ agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server.
A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation
We ask: \textit{Is it possible to retain the simplicity of a projection-based analysis without actually performing a projection step in the algorithm?}
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling.
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
In response, we introduce FedSARSA, a novel federated on-policy reinforcement learning scheme, equipped with linear function approximation, to address these challenges and provide a comprehensive finite-time error analysis.
Towards Model-Free LQR Control over Rate-Limited Channels
Toward answering this question, we study a setting where a worker agent transmits quantized policy gradients (of the LQR cost) to a server over a noiseless channel with a finite bit-rate.
Min-Max Optimization under Delays
Motivated by this gap, we examine the performance of standard min-max optimization algorithms with delayed gradient updates.
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling
Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints.
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem.
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning
In large-scale machine learning, recent works have studied the effects of compressing gradients in stochastic optimization in order to alleviate the communication bottleneck.
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 Survey of Graph-Theoretic Approaches for Analyzing the Resilience of Networked Control Systems
As the scale of networked control systems increases and interactions between different subsystems become more sophisticated, questions of the resilience of such networks increase in importance.
Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents
Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control and optimization, and reinforcement learning.
Linear Stochastic Bandits over a Bit-Constrained Channel
Specifically, in our setup, an agent interacting with an environment transmits encoded estimates of an unknown model parameter to a server over a communication channel of finite capacity.
Exploiting Heterogeneity in Robust Federated Best-Arm Identification
We study a federated variant of the best-arm identification problem in stochastic multi-armed bandits: a set of clients, each of whom can sample only a subset of the arms, collaborate via a server to identify the best arm (i. e., the arm with the highest mean reward) with prescribed confidence.
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
We consider a standard federated learning (FL) architecture where a group of clients periodically coordinate with a central server to train a statistical model.
On the Computational Complexity of the Secure State-Reconstruction Problem
To better understand this, we show that when the $\mathbf{A}$ matrix of the linear system has unitary geometric multiplicity, the gap disappears, i. e., eigenvalue observability coincides with sparse observability, and there exists a polynomial time algorithm to reconstruct the state provided the state can be reconstructed.
Near-Optimal Data Source Selection for Bayesian Learning
We then show that the data source selection problem can be transformed into an instance of the submodular set covering problem studied in the literature, and provide a standard greedy algorithm to solve the data source selection problem with provable performance guarantees.
Distributed Inference with Sparse and Quantized Communication
We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents.
Distributed Hypothesis Testing and Social Learning in Finite Time with a Finite Amount of Communication
We consider the problem of distributed hypothesis testing (or social learning) where a network of agents seeks to identify the true state of the world from a finite set of hypotheses, based on a series of stochastic signals that each agent receives.
A Communication-Efficient Algorithm for Exponentially Fast Non-Bayesian Learning in Networks
We introduce a simple time-triggered protocol to achieve communication-efficient non-Bayesian learning over a network.
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience
We study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles.
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience
Under minimal requirements on the signal structures of the agents and the underlying communication graph, we establish consistency of the proposed belief update rule, i. e., we show that the actual beliefs of the agents asymptotically concentrate on the true state almost surely.
