Yasmine Djebrouni

Yasmine Djebrouni
Laboratoire d'Informatique de Grenoble | LIG

Master of Engineering
Working on my PhD thesis : "Characterizing and Optimizing Large-Scale Machine Learning Systems".

About

6
Publications
595
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
1
Citation
Citations since 2017
6 Research Items
1 Citation
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0
20172018201920202021202220230.00.51.01.52.02.53.0

Publications

Publications (6)
Article
Full-text available
Federated Learning (FL) provides better user data privacy,while allowing users to collaboratively solve a machine learn-ing problem. However, FL can exacerbate the problem ofmodel bias and unfairness, thus, resulting in segregativeor sexist models. The objective of our PhD work is three-fold: (i) Characterize the actual impact of FL settings on bia...
Conference Paper
Full-text available
Nowadays, machine learning (ML) is widely used in many application domains to analyze datasets and build decision making systems. With the rapid growth of data, ML users switched to distributed machine learning (DML) platforms for faster executions and large-scale training datasets. However, DML platforms introduce complex execution environments th...
Conference Paper
Full-text available
Federated Learning (FL) interestingly allows a set of participants to collectively resolve a machine learning problem in a decentralized and privacy preserving manner. However, data distribution and heterogeneity, that are inherent to FL, may induce and exacerbate the problem of bias, with its prejudicial consequences such as racial or sexist segre...
Article
Full-text available
Federated Learning (FL) interestingly allows a set of participants to collectively resolve a machine learning problem in a decentralized and privacy preserving manner. However, data distribution and heterogeneity, that are inherent to FL, may induce and exacerbate the problem of bias, with its prejudicial consequences such as racial or sexist segre...
Preprint
Full-text available
Machine learning is a key for transforming data into actionable knowledge. The rapid increase in the amount of analyzed data forced the switch to distributed ML platforms. However, the complexity of such platforms is overwhelming for uninitiated users, who may not understand the trade-offs and the challenges of parameterizing such systems to achiev...
Article
Full-text available
In computer science field, according to Moore's law, the number of microprocessor components doubles every 18 months. In other terms, size of transistors is approaching the size of atoms. This law may become false because of the quantum effects at very small scales, atoms size being a fundamental barrier. Richard Feynman, a well-known physicist, su...

Network

Cited By

Projects

Project (1)