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Found 10 papers, 3 papers with code
Optimistic Online Non-stochastic Control via FTRL
This paper brings the concept of "optimism" to the new and promising framework of online Non-stochastic Control (NSC).
Adaptive Online Non-stochastic Control
We tackle the problem of Non-stochastic Control (NSC) with the aim of obtaining algorithms whose policy regret is proportional to the difficulty of the controlled environment.
Optimistic No-regret Algorithms for Discrete Caching
We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle (provided by, e. g., a Neural Network).
Online Caching with no Regret: Optimistic Learning via Recommendations
We build upon the Follow-the-Regularized-Leader (FTRL) framework, which is developed further here to include predictions for the file requests, and we design online caching algorithms for bipartite networks with pre-reserved or dynamic storage subject to time-average budget constraints.
Online Caching with Optimistic Learning
The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas.
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT).
Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey
After that, we provide a deep literature review for the applications of RL in I-health systems.
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data
Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data.
Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence
The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges.
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data
Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local models' parameters into a global model.
