Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication

面向 EdgeIoT 的横向-纵向混合联邦学习新型框架

A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

针对 EdgeIoT 中设备样本与特征异构的问题，提出 HoVeFL 混合联邦学习框架，并通过 CIFAR-10、SVHN 评估不同横纵设备配置对测试损失的影响。

面向集成感知与通信系统的个性化联邦学习驱动的波束成形优化

Personalized Federated Learning-Driven Beamforming Optimization for Integrated Sensing and Communication Systems

本文提出基于期望最大化的个性化联邦学习框架，用于集成感知与通信系统的多目标优化，使各基站自适应确定聚合权重，在通信与感知目标竞争场景下优于现有基线，实现更快收敛和更优多目标性能。

SAFE：面向遥感协同感知的自调整联邦学习框架

SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perception

本文提出SAFE自调整联邦学习框架，通过类别校正、特征对齐、双因子调制和自适应上下文增强四项策略，解决遥感协同感知中的数据分布差异、类别不平衡及通信开销问题，并在真实数据集上验证其有效性。

PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning

We explore a scheme that enables the training of a deep neural network in a Federated Learning configuration over an additive white Gaussian noise channel. The goal is to create a low complexity, linear compression strategy, called PolarAir, that reduces the size of the gradient at the user side to lower the number of channel uses needed to transmit it. The suggested approach belongs to the family of compressed sensing techniques, yet it constructs the sensing matrix and the recovery procedure using multiple access techniques. Simulations show that it can reduce the number of channel uses by ~…

Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms

Due to air quality significantly affects human health, it is becoming increasingly important to accurately and timely predict the Air Quality Index (AQI). To this end, this paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. Specifically, in the air, this framework leverages a light-weight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by Unmanned Aerial Vehicles (UAVs) for predicting AQI scale distribution. Furthermore, the Federated …

A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems

Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. In this paper, we present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems in which the central server equipped with a massive antenna array communicates with the wireless devices. One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices. To…