Search Results for author:
Found 17 papers, 2 papers with code
FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks
Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices.
SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification
We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images.
DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data
Local stochastic gradient descent (SGD) is a fundamental approach in achieving communication efficiency in Federated Learning (FL) by allowing individual workers to perform local updates.
Estimating Effects of Long-Term Treatments
We describe the identification assumptions, the estimation strategies, and the inference technique under this framework.
Frequency-dependent Switching Control for Disturbance Attenuation of Linear Systems
We show that our FDSC scheme is capable to approximate the solid in-band performance while maintaining acceptable out-of-band performance with regard to global time horizons as well as localized time horizons.
Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network
Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability.
FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation
Recent advancements in space technology have equipped low Earth Orbit (LEO) satellites with the capability to perform complex functions and run AI applications.
STSyn: Speeding Up Local SGD with Straggler-Tolerant Synchronization
Synchronous local stochastic gradient descent (local SGD) suffers from some workers being idle and random delays due to slow and straggling workers, as it waits for the workers to complete the same amount of local updates.
Improving Human Sperm Head Morphology Classification with Unsupervised Anatomical Feature Distillation
With rising male infertility, sperm head morphology classification becomes critical for accurate and timely clinical diagnosis.
Adaptive Worker Grouping For Communication-Efficient and Straggler-Tolerant Distributed SGD
Wall-clock convergence time and communication load are key performance metrics for the distributed implementation of stochastic gradient descent (SGD) in parameter server settings.
Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation
Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing recommendations to users.
Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network
The Gleason grading system using histological images is the most powerful diagnostic and prognostic predictor of prostate cancer.
A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning
Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on the ImageNet data set by using deep neural networks.
ITENE: Intrinsic Transfer Entropy Neural Estimator
Hence, the TE quantifies the improvement, as measured by the log-loss, in the prediction of the target sequence $Y$ that can be accrued when, in addition to the past of $Y$, one also has available past samples from $X$.
Boros: Secure Cross-Channel Transfers via Channel Hub
In this paper, we design and develop a novel off-chain system to shorten the routing path for the payment network.
Cryptography and Security
LAGC: Lazily Aggregated Gradient Coding for Straggler-Tolerant and Communication-Efficient Distributed Learning
Gradient-based distributed learning in Parameter Server (PS) computing architectures is subject to random delays due to straggling worker nodes, as well as to possible communication bottlenecks between PS and workers.
Coded Federated Computing in Wireless Networks with Straggling Devices and Imperfect CSI
Distributed computing platforms typically assume the availability of reliable and dedicated connections among the processors.
Information Theory Information Theory
