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Found 9 papers, 1 papers with code
Federated Learning with Flexible Control
In this paper, we address this problem and propose FlexFL - an FL algorithm with multiple options that can be adjusted flexibly.
Joint Coreset Construction and Quantization for Distributed Machine Learning
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication and computation costs.
Communication-efficient k-Means for Edge-based Machine Learning
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers.
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target object classifiers.
Sharing Models or Coresets: A Study based on Membership Inference Attack
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different approaches have been proposed: collecting and aggregating local models (federated learning) and collecting and training over representative data summaries (coreset).
A4 : Evading Learning-based Adblockers
Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful.
GLEE: Geometric Laplacian Eigenmap Embedding
Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks.
Learning and Planning in the Feature Deception Problem
In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary's preferences which are initially unknown to the defender.
Robust Coreset Construction for Distributed Machine Learning
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data.
