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Found 9 papers, 4 papers with code
Client Selection for Federated Policy Optimization with Environment Heterogeneity
This paper investigates the federated version of Approximate PI (API) and derives its error bound, taking into account the approximation error introduced by environment heterogeneity.
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback
In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming.
Intelligent Reflecting Surface-Aided Maneuvering Target Sensing: True Velocity Estimation
Maneuvering target sensing will be an important service of future vehicular networks, where precise velocity estimation is one of the core tasks.
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence
A necessary condition for the global policy to be learn-able from the local policy is also derived, which is directly related to the heterogeneity level.
Graph Neural Networks for Wireless Communications: From Theory to Practice
For design guidelines, we propose a unified framework that is applicable to general design problems in wireless networks, which includes graph modeling, neural architecture design, and theory-guided performance enhancement.
Communication-Efficient Federated Distillation with Active Data Sampling
Federated Distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, which achieves orders of magnitude reduction of the communication overhead compared with FedAvg and is flexible to handle heterogeneous models at the clients.
Learn to Communicate with Neural Calibration: Scalability and Generalization
Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models.
Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems.
Client-Edge-Cloud Hierarchical Federated Learning
To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation.
