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Found 67 papers, 8 papers with code
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs
In CRNs, both spectrum sensing and resource allocation (SSRA) are critical to maximizing system throughput while minimizing collisions of secondary users with the primary network.
Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels
Specifically, we introduce a \textit{smart information push-pull} methodology for data/embedding exchange tailored to FL settings with either soft or strict data privacy restrictions.
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Moreover, compared to synchronous FedPG, AFedPG improves the time complexity from $\mathcal{O}(\frac{t_{\max}}{N})$ to $\mathcal{O}(\frac{1}{\sum_{i=1}^{N} \frac{1}{t_{i}}})$, where $t_{i}$ denotes the time consumption in each iteration at the agent $i$, and $t_{\max}$ is the largest one.
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
Numerical analysis shows the advantages in terms of convergence speed and straggler resilience of the proposed method to different available FL schemes and benchmark datasets.
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
A recent line of research has been investigating deep learning approaches to wireless positioning (WP).
Decentralized Sporadic Federated Learning: A Unified Methodology with Generalized Convergence Guarantees
Decentralized Federated Learning (DFL) has received significant recent research attention, capturing settings where both model updates and model aggregations -- the two key FL processes -- are conducted by the clients.
Rethinking the Starting Point: Enhancing Performance and Fairness of Federated Learning via Collaborative Pre-Training
Most existing federated learning (FL) methodologies have assumed training begins from a randomly initialized model.
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
Multimodal federated learning (FL) aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities.
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
Federated learning (FL) is a promising approach for solving multilingual tasks, potentially enabling clients with their own language-specific data to collaboratively construct a high-quality neural machine translation (NMT) model.
Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches
In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users.
Fault-Tolerant Vertical Federated Learning on Dynamic Networks
Vertical Federated learning (VFL) is a class of FL where each client shares the same sample space but only holds a subset of the features.
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading
While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services.
The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks
This paper addresses this gap by analyzing the performance of decentralized FL for various adversarial placement strategies when adversaries can jointly coordinate their placement within a network.
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Our optimization methodology aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy while minimizing data processing and D2D communication resource consumption subject to realistic constraints on the network topology and device capabilities.
Submodel Partitioning in Hierarchical Federated Learning: Algorithm Design and Convergence Analysis
Hierarchical federated learning (HFL) has demonstrated promising scalability advantages over the traditional "star-topology" architecture-based federated learning (FL).
Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems
Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware.
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication
Multimodal federated learning (FL) aims to enrich model training in FL settings where devices are collecting measurements across multiple modalities (e. g., sensors measuring pressure, motion, and other types of data).
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV).
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning
Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchange.
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
In the second strategy, the non-dropped intermediate feature and gradient vectors are quantized using adaptive quantization levels determined based on the ranges of the vectors.
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
In this work, we first reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions.
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization
Our analysis sheds light on the joint impact of device training variables (e. g., number of local gradient descent steps), asynchronous scheduling decisions (i. e., when a device trains a task), and dynamic data drifts on the performance of ML training for different tasks.
Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement
For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions.
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation
Our methodology, Source-Target Determination and Link Formation (ST-LF), optimizes both (i) classification of devices into sources and targets and (ii) source-target link formation, in a manner that considers the trade-off between ML model accuracy and communication energy efficiency.
On the Effects of Data Heterogeneity on the Convergence Rates of Distributed Linear System Solvers
Using this notion, we bound and compare the convergence rates of the studied algorithms and capture the effects of both cross-machine and local data heterogeneity on these quantities.
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks
Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks.
Challenges and Opportunities for Beyond-5G Wireless Security
The demand for broadband wireless access is driving research and standardization of 5G and beyond-5G wireless systems.
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices.
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices.
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
In this work, we reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions.
A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios.
SplitGP: Achieving Both Generalization and Personalization in Federated Learning
A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i. e., to individual clients) and generalization (i. e., to unseen data) properties concurrently.
Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders
Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks.
Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices
We theoretically demonstrate that our methodology converges to the globally optimal learning model at a $O{(\frac{\ln{k}}{\sqrt{k}})}$ rate under standard assumptions in distributed learning and consensus literature.
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead.
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange
In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data.
A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions
Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment.
Nonparametric Decentralized Detection and Sparse Sensor Selection via Multi-Sensor Online Kernel Scalar Quantization
Moreover, we are interested in sparse sensor selection using a marginalized weighted kernel approach to improve network resource efficiency by disabling less reliable sensors with minimal effect on classification performance. To achieve our goals, we develop a multi-sensor online kernel scalar quantization (MSOKSQ) learning strategy that operates on the sensor outputs at the fusion center.
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients.
Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds
Through theoretical analysis, we demonstrate that our methodology achieves asymptotic convergence to the globally optimal learning model under standard assumptions in distributed learning and graph consensus literature, and without restrictive connectivity requirements on the underlying topology.
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point
CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility.
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing
To assist the ML model training for resource-constrained MDs, we develop an offloading strategy that enables MDs to transmit their data to one of the associated ESs.
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
PSL considers the realistic scenario where global aggregations are conducted with idle times in-between them for resource efficiency improvements, and incorporates data dispersion and model dispersion with local model condensation into FedL.
Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity
Federated learning (FL) has emerged as a popular technique for distributing machine learning across wireless edge devices.
Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
We consider a practical setting where (i) the IRS reflection coefficients are achieved by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate.
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach
Our methodology for predicting in-video quiz performance is based on three key ideas we develop.
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling
Federated learning has emerged as a popular technique for distributing model training across the network edge.
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach
The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to address computational inadequacy of devices by conducting model training among the UAV swarms, and (iv) model/concept drift to model time-varying data distributions.
A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification
Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model.
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations
Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge.
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach
Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates.
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server.
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Despite the recent successes of deep learning in natural language processing (NLP), there remains widespread usage of and demand for techniques that do not rely on machine learning.
Frequency-based Automated Modulation Classification in the Presence of Adversaries
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals.
Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach
Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently.
A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions
However, the problem of allocating items among the bidders to maximize the auctioneers" revenue, i. e., the winner determination problem (WDP), is NP-complete to solve and inapproximable.
Federated Learning with Communication Delay in Edge Networks
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks.
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation
In this work, we reexamine the inter-related problems of "topic identification" and "text segmentation" for sparse document learning, when there is a single new text of interest.
Fast-Convergent Federated Learning
In this paper, we propose a fast-convergent federated learning algorithm, called FOLB, which performs intelligent sampling of devices in each round of model training to optimize the expected convergence speed.
Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks
Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale.
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks
We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e. g., the spectral radius) and the learning algorithm (e. g., the number of D2D rounds in different clusters).
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks
There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices.
Network-Aware Optimization of Distributed Learning for Fog Computing
Unlike traditional federated learning frameworks, our method enables devices to offload their data processing tasks to each other, with these decisions determined through a convex data transfer optimization problem that trades off costs associated with devices processing, offloading, and discarding data points.
Distributed, Parallel, and Cluster Computing
Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing
In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency.
Networking and Internet Architecture Signal Processing
A Deep Learning Approach to Behavior-Based Learner Modeling
The increasing popularity of e-learning has created demand for improving online education through techniques such as predictive analytics and content recommendations.
Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing
Pricing assets has attracted significant attention from the financial technology community.
