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Found 59 papers, 5 papers with code
Harnessing the Power of AI-Generated Content for Semantic Communication
Recognizing the transformative capabilities of AI-generated content (AIGC) technologies in content generation, this paper explores a pioneering approach by integrating them into SemCom to address the aforementioned challenges.
Agent-driven Generative Semantic Communication for Remote Surveillance
In the era of 6G, featuring compelling visions of intelligent transportation system, digital twins, remote surveillance is poised to become a ubiquitous practice.
Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity
This paper deals with federated learning (FL) in the presence of malicious Byzantine attacks and data heterogeneity.
Adaptive Federated Learning Over the Air
In contrast, an Adam-like algorithm converges at the $\mathcal{O}( 1/T )$ rate, demonstrating its advantage in expediting the model training process.
RIS-Enhanced Cognitive Integrated Sensing and Communication: Joint Beamforming and Spectrum Sensing
Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks.
Spectral Co-Distillation for Personalized Federated Learning
Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously.
Knowledge Graph Driven UAV Cognitive Semantic Communication Systems for Efficient Object Detection
Unmanned aerial vehicles (UAVs) are widely used for object detection.
OFDM-Based Digital Semantic Communication with Importance Awareness
Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance.
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used.
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification
Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients' data.
Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration
Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI).
Analysis of Age of Information in Non-terrestrial Networks
Our work fills a gap in the literature by providing a comprehensive analysis of AoI in NTN and offers new insights into the performance of LEO satellite networks.
The Role of Federated Learning in a Wireless World with Foundation Models
Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications.
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework
To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL.
Optimizing Cache Content Placement in Integrated Terrestrial and Non-terrestrial Networks
Non-terrestrial networks (NTN) offer potential for efficient content broadcast in remote regions, thereby extending the reach of digital services.
Novel Online-Offline MA2C-DDPG for Efficient Spectrum Allocation and Trajectory Optimization in Dynamic Spectrum Sharing UAV Networks
Unmanned aerial vehicle (UAV) communication is of crucial importance for diverse practical applications.
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
Federated edge learning is envisioned as the bedrock of enabling intelligence in next-generation wireless networks, but the limited spectral resources often constrain its scalability.
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications
We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare.
Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks
In this paper, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS).
DPP-based Client Selection for Federated Learning with Non-IID Data
This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL's data heterogeneity issue.
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence
With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence.
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks.
Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems
However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge.
Personalizing Federated Learning with Over-the-Air Computations
Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue.
Differentially Private Deep Q-Learning for Pattern Privacy Preservation in MEC Offloading
Mobile edge computing (MEC) is a promising paradigm to meet the quality of service (QoS) requirements of latency-sensitive IoT applications.
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework
In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm.
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
In edge computing, users' service profiles are migrated due to user mobility.
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks
Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.
Towards Federated Long-Tailed Learning
Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks.
FedCorr: Multi-Stage Federated Learning for Label Noise Correction
Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL.
Federated Stochastic Gradient Descent Begets Self-Induced Momentum
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data.
Networking of Internet of UAVs: Challenges and Intelligent Approaches
Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs.
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching
In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks.
Optimal Distribution Design for Irregular Repetition Slotted ALOHA with Multi-Packet Reception
Associated with multi-packet reception at the access point, irregular repetition slotted ALOHA (IRSA) holds a great potential in improving the access capacity of massive machine type communication systems.
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks
Finally, we provide experiments to evaluate the learning performance of HFL and our MACFL.
Dynamic Attention-based Communication-Efficient Federated Learning
Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the clients.
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach
In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset, and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.
Learning Autonomy in Management of Wireless Random Networks
The DMPNN is investigated for various configurations of the power control in wireless networks, and intensive numerical results prove its universality and viability over conventional optimization and DNN approaches.
Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning
This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users.
Training Classifiers that are Universally Robust to All Label Noise Levels
In particular, we shall assume that a small subset of any given noisy dataset is known to have correct labels, which we treat as "positive", while the remaining noisy subset is treated as "unlabeled".
Ranked #7 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
Two-Stage Stochastic Optimization via Primal-Dual Decomposition and Deep Unrolling
Then we propose a PDD-based stochastic successive convex approximation (PDD-SSCA) algorithmic framework to find KKT solutions for two-stage stochastic optimization problems.
Non-Terrestrial Networks for UAVs: Base Station Service Provisioning Schemes with Antenna Tilt
By considering the antenna tilt angle-based channel gain, we derive the network outage probability for both IS-BS and ES-BS schemes, and show the existence of the optimal tilt angle that minimizes the network outage probability after analyzing the conflict impact of the antenna tilt angle.
Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report
cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.
Joint Network Topology Inference via Structured Fusion Regularization
Theoretically, we provide a theoretical analysis of the proposed graph estimator, which establishes a non-asymptotic bound of the estimation error under the high-dimensional setting and reflects the effect of several key factors on the convergence rate of our algorithm.
Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network
Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique.
Networking and Internet Architecture Signal Processing
Power Control for a URLLC-enabled UAV system incorporated with DNN-Based Channel Estimation
This problem is challenging to be solved due to the requirement of analytically tractable channel models and the non-convex characteristic as well.
Multi-Armed Bandit Based Client Scheduling for Federated Learning
In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels.
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing
We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets.
Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing
We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.
Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
Federated learning (FL) is a machine learning model that preserves data privacy in the training process.
Information Theory Signal Processing Information Theory
A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver
For universal support of arbitrary dimming target, the DL-based VLC transceiver is trained with multiple dimming constraints, which turns out to be a constrained training optimization that is very challenging to handle with existing DL methods.
Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches
To address the power allocation problem, two novel methods are proposed.
On Safeguarding Privacy and Security in the Framework of Federated Learning
Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).
Networking and Internet Architecture
Scheduling Policies for Federated Learning in Wireless Networks
Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration.
Information Theory Signal Processing Information Theory
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management
This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations.
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate.
Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method
Despite enjoying extensive applications in video analysis, three-dimensional convolutional neural networks (3D CNNs)are restricted by their massive computation and storage consumption.
Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks
The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the hope of discovering more efficient methods for training artificial networks.
