15 citations · 109 across the 26 of their papers we have counts for
28 papers
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…
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity
Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui +3
Byzantine machine learning (ML) aims to ensure the resilience of distributed learning algorithms to misbehaving (or Byzantine) machines. Although this problem received significant…
On the Privacy-Robustness-Utility Trilemma in Distributed Learning
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta +2
The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversari…
Impact of Redundancy on Resilience in Distributed Optimization and Learning
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
This report considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, w…
On the Impossible Safety of Large AI Models
El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui +5
Large AI Models (LAIMs), of which large language models are the most prominent recent example, showcase some impressive performance. However they have been empirically found to pos…
Robust Collaborative Learning with Linear Gradient Overhead
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta +3
Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware…