36 citations · 60 across the 10 of their papers we have counts for
10 papers
ByzFL: Research Framework for Robust Federated Learning
Marc González, Rachid Guerraoui, Rafael Pinot +3
We present ByzFL, an open-source Python library for developing and benchmarking robust federated learning (FL) algorithms. ByzFL provides a unified and extensible framework that in…
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
Beatriz Borges, Negar Foroutan, Deniz Bayazit +87
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they a…
Overcoming the Challenges of Batch Normalization in Federated Learning
Rachid Guerraoui, Rafael Pinot, Geovani Rizk +2
Batch normalization has proven to be a very beneficial mechanism to accelerate the training and improve the accuracy of deep neural networks in centralized environments. Yet, the s…
Adaptive Gradient Clipping for Robust Federated Learning
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta +3
Robust federated learning aims to maintain reliable performance despite the presence of adversarial or misbehaving workers. While state-of-the-art (SOTA) robust distributed gradien…
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
Youssef Allouah, Sadegh Farhadkhani, Rachid GuerraouI +4
The possibility of adversarial (a.k.a., {\em Byzantine}) clients makes federated learning (FL) prone to arbitrary manipulation. The natural approach to robustify FL against adversa…
Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta +2
The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogen…
An -No-Regret Algorithm For Graphical Bilinear Bandits
Geovani Rizk, Igor Colin, Albert Thomas +2
We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where agents in a graph play a stochastic bilinear bandit game with each of their neighbor…
Refined bounds for randomized experimental design
Geovani Rizk, Igor Colin, Albert Thomas +1
Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion. In the context of linear regression, several op…
Best Arm Identification in Graphical Bilinear Bandits
Geovani Rizk, Albert Thomas, Igor Colin +2
We introduce a new graphical bilinear bandit problem where a learner (or a \emph{central entity}) allocates arms to the nodes of a graph and observes for each edge a noisy bilinear…
Randomization matters. How to defend against strong adversarial attacks
Rafael Pinot, Raphael Ettedgui, Geovani Rizk +2
Is there a classifier that ensures optimal robustness against all adversarial attacks? This paper answers this question by adopting a game-theoretic point of view. We show that adv…