Local stochastic gradient descent (SGD) is a fundamental approach in
ach...
The widespread adoption of edge computing has emerged as a prominent tre...
Cloud native technology has revolutionized 5G beyond and 6G communicatio...
Wall-clock convergence time and communication rounds are critical perfor...
A major bottleneck of distributed learning under parameter-server (PS)
f...
Synchronous local stochastic gradient descent (local SGD) suffers from s...
With rising male infertility, sperm head morphology classification becom...
Wall-clock convergence time and communication load are key performance
m...
Today's research in recommender systems is largely based on experimental...
Matrix factorization is an important representation learning algorithm, ...
The Gleason grading system using histological images is the most powerfu...
Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-traine...
A multi-cell mobile edge computing network is studied, in which each use...
Quantifying the directionality of information flow is instrumental in
un...
The payment channel, which allows two parties to perform micropayments
w...
This work identifies the fundamental limits of cache-aided coded multica...
Gradient-based distributed learning in Parameter Server (PS) computing
a...
Consider a mobile edge computing system in which users wish to obtain th...
This paper considers a cloud-RAN architecture with cache-enabled
multi-a...
Distributed computing platforms typically assume the availability of rel...
In a distributed computing system operating according to the
map-shuffle...
In a Fog Radio Access Network (F-RAN), the cloud processor (CP) collects...
In cellular systems, content delivery latency can be minimized by jointl...