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Found 17 papers, 6 papers with code
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning
We test our algorithm in an ITS environment with edge cloud computing.
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks
Alternative solutions have been surfaced (e. g. Split Learning, Federated Learning), distributing AI tasks of reduced complexity across nodes, while preserving the privacy of the data.
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia.
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more and more infeasible.
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks
Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy.
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
If the required memory to train a model exceeds this limit, the device will be excluded from the training.
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing.
Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application
We formulate offloading of computational tasks from a dynamic group of mobile agents (e. g., cars) as decentralized decision making among autonomous agents.
Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning
To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications \textit{before} vehicles leave the cellular network coverage.
Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions
We formulate computation offloading as a decentralized decision-making problem with autonomous agents.
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games
We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and learns with partial information in a dynamic environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
To adapt to the devices' heterogeneous resources, CoCoFL freezes and quantizes selected layers, reducing communication, computation, and memory requirements, whereas other layers are still trained in full precision, enabling to reach a high accuracy.
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems
We study the problem of distributed training of neural networks (NNs) on devices with heterogeneous, limited, and time-varying availability of computational resources.
Self-Driving Network and Service Coordination Using Deep Reinforcement Learning
While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e. g., a priori knowledge).
Distributed Learning on Heterogeneous Resource-Constrained Devices
We consider a distributed system, consisting of a heterogeneous set of devices, ranging from low-end to high-end.
VRLS: A Unified Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications
VRLS is a unified reinforcement learning (RL) solution, wherein the learning agent, the state representation, and the reward provided to the agent are applicable to different vehicular environments of interest (in terms of vehicular density, resource configuration, and wireless channel conditions).
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage
Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e. g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles.
