5 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…
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…
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…
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…
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…