Search Results for author:
Found 42 papers, 8 papers with code
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
In this paper, we propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously.
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence.
Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations
This study focuses on the deepfake detection of facial images of individual public figures.
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i. e., FedPepTAO, to enable efficient and effective FL of LLMs.
CGBA: Curvature-aware Geometric Black-box Attack
While the proposed CGBA attack can work effectively for an arbitrary decision boundary, it is particularly efficient in exploiting the low curvature to craft high-quality adversarial examples, which is widely seen and experimentally verified in commonly used classifiers under non-targeted attacks.
Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity
While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth.
GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds
In this paper, we propose a graph neural network-augmented deep reinforcement learning scheme (GA-DRL) for scheduling DAG tasks over dynamic VCs.
Distributed Learning over Networks with Graph-Attention-Based Personalization
In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model.
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity
Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks.
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability.
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of next-generation wireless communications.
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning
The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process.
Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles
This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (CPUs) and local datasets to train a global model.
Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks
As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems.
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
Prior work has shown that the gradient sharing strategies in federated learning can be vulnerable to data reconstruction attacks.
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles
To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold.
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server
Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i. e., limited computational resources and low training efficiency.
Efficient Device Scheduling with Multi-Job Federated Learning
However, the scheduling of devices for multiple jobs with FL remains a critical and open problem.
Neural Tangent Kernel Empowered Federated Learning
The paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the conventional FL paradigms.
Federated Learning via Plurality Vote
Federated learning allows collaborative workers to solve a machine learning problem while preserving data privacy.
On the exploitative behavior of adversarial training against adversarial attacks
Adversarial attacks have been developed as intentionally designed perturbations added to the inputs in order to fool deep neural network classifiers.
60 GHz Outdoor Propagation Measurements and Analysis Using Facebook Terragraph Radios
In this work, we investigated the propagation and scattering behavior of 60 GHz mmWave signals in outdoor environments at a travel distance of 98 m for an aerial link (rooftop to rooftop), and 147 m for a ground link (light-pole to light-pole).
Experimental Study of Outdoor UAV Localization and Tracking using Passive RF Sensing
First, the Keysight sensor detects the UAV by comparing the received RF signature with various other UAVs' RF signatures in the Keysight database using an envelope detection algorithm.
Channel Rank Improvement in Urban Drone Corridors Using Passive Intelligent Reflectors
Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones.
Communication-Efficient Federated Learning via Predictive Coding
In each communication round, we select the predictor and quantizer based on the rate-distortion cost, and further reduce the redundancy with entropy coding.
Adversarial training may be a double-edged sword
Adversarial training has been shown as an effective approach to improve the robustness of image classifiers against white-box attacks.
Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors
The concept of drone corridors is recently getting more attention to enable connected, safe, and secure flight zones in the national airspace.
Precoder Design for Physical-Layer Security and Authentication in Massive MIMO UAV Communications
Supporting reliable and seamless wireless connectivity for unmanned aerial vehicles (UAVs) has recently become a critical requirement to enable various different use cases of UAVs.
Decentralized Inference with Graph Neural Networks in Wireless Communication Systems
Different from other neural network models, GNN can be implemented in a decentralized manner with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems.
Modeling the Nonsmoothness of Modern Neural Networks
Modern neural networks have been successful in many regression-based tasks such as face recognition, facial landmark detection, and image generation.
Power Allocation for Fingerprint-Based PHY-Layer Authentication with mmWave UAV Networks
Physical layer security (PLS) techniques can help to protect wireless networks from eavesdropper attacks.
Distributed ADMM with Synergetic Communication and Computation
In this paper, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system.
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.
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.
Lifetime Maximization for UAV-assisted Data Gathering Networks in the Presence of Jamming
Deployment of unmanned aerial vehicles (UAVs) is recently getting significant attention due to a variety of practical use cases, such as surveillance, data gathering, and commodity delivery.
Communication Efficient Federated Learning with Energy Awareness over Wireless Networks
Moreover, most of the existing works assume Channel State Information (CSI) available at both the mobile devices and the parameter server, and thus the mobile devices can adopt fixed transmission rates dictated by the channel capacity.
GeoDA: a geometric framework for black-box adversarial attacks
We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-$1$ label of the classifier.
Accelerating Generalized Benders Decomposition for Wireless Resource Allocation
Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced.
Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees
We present Stochastic-Sign SGD which utilizes novel stochastic-sign based gradient compressors enabling the aforementioned properties in a unified framework.
Distributed Byzantine Tolerant Stochastic Gradient Descent in the Era of Big Data
The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources.
Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent
While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals.
