555 citations · 1k across the 61 of their papers we have counts for
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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…
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
Martijn de Vos, Sadegh Farhadkhani, Rachid Guerraoui +3
We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence…
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…
Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores
Diana Petrescu, Arsany Guirguis, Do Le Quoc +3
Storage disaggregation underlies today's cloud and is naturally complemented by pushing down some computation to storage, thus mitigating the potential network bottleneck between t…
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…