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Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of multi-task learning.
Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access
Recognizing the computational constraints and trust issues associated with on-device computation, we propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center, where a decision is made to identify whether a potential transmitter is present, or not.
Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming
The encoded bitrate and the quality of the compressed video depend on encoder parameters, specifically, the quantization parameter (QP).
Joint Sensing and Semantic Communications with Multi-Task Deep Learning
The transmitter employs a deep neural network, namely an encoder, for joint operations of source coding, channel coding, and modulation, while the receiver utilizes another deep neural network, namely a decoder, for joint operations of demodulation, channel decoding, and source decoding to reconstruct the data samples.
A Learning Based Scheme for Fair Timeliness in Sparse Gossip Networks
We consider a gossip network, consisting of $n$ nodes, which tracks the information at a source.
Deep Learning Based Uplink Multi-User SIMO Beamforming Design
The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensive coverage, minimal latency and energy-efficient performance.
Deep Learning-Based Real-Time Rate Control for Live Streaming on Wireless Networks
The objective is to maintain an encoded video bitrate slightly below the available channel bitrate.
Semantic Multi-Resolution Communications
The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers.
Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning
A multi-task deep learning approach that involves training a common encoder at the transmitter and individual decoders at the receivers is presented for joint optimization of completing multiple tasks and communicating with multiple receivers.
Timely Asynchronous Hierarchical Federated Learning: Age of Convergence
The goal of each client is to converge to the global model, while maintaining timeliness of the clients, i. e., having optimum training iteration time.
Age of Information in Deep Learning-Driven Task-Oriented Communications
This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter.
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs
Our algorithm uses exchanged gradients to calculate the correlations among tasks automatically, and dynamically adjusts the communication graph to connect mutually beneficial tasks and isolate those that may negatively impact each other.
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks
The backdoor attack can effectively change the semantic information transferred for the poisoned input samples to a target meaning.
Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks
By augmenting the reconstruction loss with a semantic loss, the two deep neural networks (DNNs) of this encoder-decoder pair are interactively trained with the DNN of the semantic task classifier.
Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects
In this paper, wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label.
Hierarchical Over-the-Air FedGradNorm
MTL can be integrated into a federated learning (FL) setting if tasks are distributed across clients and clients have a single shared network, leading to personalized federated learning (PFL).
Private Federated Submodel Learning with Sparsification
We investigate the problem of private read update write (PRUW) in federated submodel learning (FSL) with sparsification.
Dynamic SAFFRON: Disease Control Over Time Via Group Testing
We characterize the performance of dynamic individual testing algorithm and introduce a novel dynamic SAFFRON based group testing algorithm.
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning
In federated settings, the statistical heterogeneity due to different task complexities and data heterogeneity due to non-iid nature of local datasets can both degrade the learning performance of the system.
Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces
Results from different network topologies show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications.
Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications
The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output.
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges
Most of our lives are conducted in the cyberspace.
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond
Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications.
Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning
Distributed implementations are crucial in speeding up large scale machine learning applications.
Group Testing with a Graph Infection Spread Model
We propose a class of two-step sampled group testing algorithms where we exploit the known probabilistic infection spread model.
Information Theory Computers and Society Data Structures and Algorithms Networking and Internet Architecture Signal Processing Information Theory
Timely Communication in Federated Learning
Under the proposed scheme, at each iteration, the PS waits for $m$ available clients and sends them the current model.
Timely Tracking of Infection Status of Individuals in a Population
We observe that if the total test rate is limited, instead of testing all members of the population equally, only a portion of the population is tested based on their infection and recovery rates.
Computers and Society Physics and Society
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers
The transmitter is equipped with a deep neural network (DNN) classifier for detecting the ongoing transmissions from the background emitter and transmits a signal if the spectrum is idle.
Gradient Coding with Dynamic Clustering for Straggler Mitigation
In distributed synchronous gradient descent (GD) the main performance bottleneck for the per-iteration completion time is the slowest \textit{straggling} workers.
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers
First, we show that multiple independent adversaries, each with a single antenna cannot improve the attack performance compared to a single adversary with multiple antennas using the same total power.
Coded Distributed Computing with Partial Recovery
In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation.
Age-Based Coded Computation for Bias Reduction in Distributed Learning
To mitigate biased estimators, we design a $timely$ dynamic encoding framework for partial recovery that includes an ordering operator that changes the codewords and computation orders at workers over time.
How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy
We consider the problem of hiding wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect whether any transmission of interest is present or not.
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
There is a transmitter that transmits signals with different modulation types.
Straggler-aware Distributed Learning: Communication Computation Latency Trade-off
When gradient descent (GD) is scaled to many parallel workers for large scale machine learning problems, its per-iteration computation time is limited by the straggling workers.
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels
In the meantime, the adversary makes over-the-air transmissions that are received as superimposed with the transmitter's signals to fool the classifier at the receiver into making errors.
Gradient Coding with Clustering and Multi-message Communication
Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the parameters of the model in an iterative fashion.
Distributed Gradient Descent with Coded Partial Gradient Computations
Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity.
Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers
In most of the existing DGD schemes, either with coded computation or coded communication, the non-straggling CSs transmit one message per iteration once they complete all their assigned computation tasks.
