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Found 11 papers, 2 papers with code
Compressed Federated Reinforcement Learning with a Generative Model
Addressing this challenge, federated reinforcement learning (FedRL) has emerged, wherein agents collaboratively learn a single policy by aggregating local estimations.
Distributed Momentum Methods Under Biased Gradient Estimations
In this work, we establish non-asymptotic convergence bounds on distributed momentum methods under biased gradient estimation on both general non-convex and $\mu$-PL non-convex problems.
On the Convergence of Federated Learning Algorithms without Data Similarity
In this paper, we present a novel and unified framework for analyzing the convergence of federated learning algorithms without the need for data similarity conditions.
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance
The objective of releasing this dataset is to give a broad range of researchers the possibility of working with real-world data from an internationally well-known company and introduce a standard benchmark to the predictive maintenance field, fostering reproducible research.
Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms
In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks.
Human-Inspired Framework to Accelerate Reinforcement Learning
This paper introduces a novel human-inspired framework to enhance RL algorithm sample efficiency.
On the Convergence of Step Decay Step-Size for Stochastic Optimization
The convergence of stochastic gradient descent is highly dependent on the step-size, especially on non-convex problems such as neural network training.
The Internet of Things as a Deep Neural Network
To ensure communication efficiency, this article proposes to model the measurement compression at IoT nodes and the inference at the base station or cloud as a deep neural network (DNN).
A flexible framework for communication-efficient machine learning: from HPC to IoT
With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes.
Compressed Gradient Methods with Hessian-Aided Error Compensation
Several gradient compression techniques have been proposed to reduce the communication load at the price of a loss in solution accuracy.
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication
The communication time of these algorithms follows a complex interplay between a) the algorithm's convergence properties, b) the compression scheme, and c) the transmission rate offered by the digital channel.
