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Found 12 papers, 3 papers with code
Effective Communication with Dynamic Feature Compression
The remote wireless control of industrial systems is one of the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that need to be shared over the wireless medium may overload even high-capacity connections.
A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT
Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes underlying the production chains.
Adaptive Compression in Federated Learning via Side Information
The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL).
Semantic Communication of Learnable Concepts
The transmitter applies a learning algorithm to the available examples, and extracts knowledge from the data by optimizing a probability distribution over a set of models, i. e., known functions, which can better describe the observed data, and so potentially the underlying concepts.
Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression
The coordination of robotic swarms and the remote wireless control of industrial systems are among the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that needs to be shared over the wireless medium can overload even high-capacity connections.
Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach
This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks.
Sparse Random Networks for Communication-Efficient Federated Learning
At the end of the training, the final model is a sparse network with random weights -- or a subnetwork inside the dense random network.
Rate-Constrained Remote Contextual Bandits
We consider a rate-constrained contextual multi-armed bandit (RC-CMAB) problem, in which a group of agents are solving the same contextual multi-armed bandit (CMAB) problem.
Remote Contextual Bandits
We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel.
Contextual Multi-Armed Bandit with Communication Constraints
We consider a remote Contextual Multi-Armed Bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel.
On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI
Federated learning (FL) has recently emerged as an attractive decentralized solution for wireless networks to collaboratively train a shared model while keeping data localized.
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control
The proposed framework relies on the possibility for the UAVs to exchange some information through a communication channel, in order to achieve context-awareness and implicitly coordinate the swarm's actions.
