Similarity caching allows requests for an item to be served by a similar...
Online learning algorithms have been successfully used to design caching...
Federated learning (FL) has enabled training machine learning models
exp...
The enormous amount of data produced by mobile and IoT devices has motiv...
Federated learning (FL) is an effective solution to train machine learni...
In this paper, we initiate the study of local model reconstruction attac...
Similarity caching allows requests for an item i to be served by a
simil...
Count-Min Sketch with Conservative Updates (CMS-CU) is a popular algorit...
Federated learning allows clients to collaboratively learn statistical m...
In cross-device federated learning (FL) setting, clients such as mobiles...
The increasing size of data generated by smartphones and IoT devices
mot...
Caching systems have long been crucial for improving the performance of ...
We present the novel idea of inference delivery networks (IDN), networks...
Similarity caching systems have recently attracted the attention of the
...
We study an online caching problem in which requests can be served by a ...
Federated learning usually employs a client-server architecture where an...
The most popular framework for distributed training of machine learning
...
Consensus-based distributed optimization methods have recently been advo...
We consider a dense cellular network, in which a limited-size cache is
a...
This paper focuses on similarity caching systems, in which a user reques...
We propose a caching policy that uses a feedforward neural network (FNN)...
We focus on a dense cellular network, in which a limited-size cache is
a...
We consider elastic resource provisioning in the cloud, focusing on in-m...
Demand-Response (DR) programs, whereby users of an electricity network a...
"Geographic Load Balancing" is a strategy for reducing the energy cost o...