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Found 26 papers, 4 papers with code
BA-BFL: Barycentric Aggregation for Bayesian Federated Learning
In this work, we study the problem of aggregation in the context of Bayesian Federated Learning (BFL).
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture.
Alternating Minimization Schemes for Computing Rate-Distortion-Perception Functions with $f$-Divergence Perception Constraints
We employ the developed solutions in an alternating minimization scheme, namely Optimal Alternating Minimization (OAM), for which we provide convergence guarantees.
Integrated Push-and-Pull Update Model for Goal-Oriented Effective Communication
This paper studies decision-making for goal-oriented effective communication.
DRACO: Decentralized Asynchronous Federated Learning over Continuous Row-Stochastic Network Matrices
Recent developments and emerging use cases, such as smart Internet of Things (IoT) and Edge AI, have sparked considerable interest in the training of neural networks over fully decentralized (serverless) networks.
Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents.
A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data
This study presents a new approach for estimating confidence in machine learning model predictions, specifically in regression tasks utilizing Earth Observation (EO) data, with a particular focus on mosquito abundance (MA) estimation.
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
We show that the generalization performance of a client can be improved only by collaborating with other clients that have more training data and similar data distribution.
From OTFS to AFDM: A Comparative Study of Next-Generation Waveforms for ISAC in Doubly-Dispersive Channels
Next-generation wireless systems will offer integrated sensing and communications (ISAC) functionalities not only in order to enable new applications, but also as a means to mitigate challenges such as doubly-dispersive channels, which arise in high mobility scenarios and/or at millimeter-wave (mmWave) and Terahertz (THz) bands.
On the Computation of the Gaussian Rate-Distortion-Perception Function
In this paper, we study the computation of the rate-distortion-perception function (RDPF) for a multivariate Gaussian source under mean squared error (MSE) distortion and, respectively, Kullback-Leibler divergence, geometric Jensen-Shannon divergence, squared Hellinger distance, and squared Wasserstein-2 distance perception metrics.
AFDM vs OTFS: A Comparative Study of Promising Waveforms for ISAC in Doubly-Dispersive Channels
**PLEASE FIND THE FULL EXTENDED ARTICLE "From OTFS to AFDM: A Comparative Study of Next-Generation Waveforms for ISAC in Doubly-Dispersive Channels" (Accepted for publication at the IEEE Signal Processing Magazine - Special Issue on Signal Processing for the Integrated Sensing and Communications Revolution)** This white paper aims to briefly describe a proposed article that will provide a thorough comparative study of waveforms designed to exploit the features of doubly-dispersive channels arising in heterogeneous high-mobility scenarios as expected in the beyond fifth generation (B5G) and sixth generation (6G), in relation to their suitability to integrated sensing and communications (ISAC) systems.
Blind Asynchronous Goal-Oriented Detection for Massive Connectivity
Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users.
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
Classic results show that the IW correction is needed when the model is parametric and misspecified.
Personalized Decentralized Federated Learning with Knowledge Distillation
To cope with this issue, we propose a personalized and fully decentralized FL algorithm, leveraging knowledge distillation techniques to empower each device so as to discern statistical distances between local models.
Goal-Oriented Communications for the IoT and Application to Data Compression
Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decision processes of smart applications.
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
In this context, we explore the application of the framework of robust Bayesian learning.
Communication-Efficient Distributionally Robust Decentralized Learning
Decentralized learning algorithms empower interconnected devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator.
Robust PAC$^m$: Training Ensemble Models Under Misspecification and Outliers
Standard Bayesian learning is known to have suboptimal generalization capabilities under misspecification and in the presence of outliers.
UAV-Aided Decentralized Learning over Mesh Networks
Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication.
Towards Disentangling Information Paths with Coded ResNeXt
We propose a neural network architecture for classification, in which the information that is relevant to each class flows through specific paths.
Asynchronous Decentralized Learning over Unreliable Wireless Networks
Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireless networks with fixed topologies and reliable workers.
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility.
Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks
The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here.
Team Deep Mixture of Experts for Distributed Power Control
In the context of wireless networking, it was recently shown that multiple DNNs can be jointly trained to offer a desired collaborative behaviour capable of coping with a broad range of sensing uncertainties.
Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain.
Information Theory Information Theory
Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users
Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user---the two extremes in data stream allocation.
Information Theory Information Theory
