10 citations · 50 across the 13 of their papers we have counts for
13 papers
Robust Federated Inference
Akash Dhasade, Sadegh Farhadkhani, Rachid Guerraoui +4
Federated inference, in the form of one-shot federated learning, edge ensembles, or federated ensembles, has emerged as an attractive solution to combine predictions from multiple…
On the Relevance of Byzantine Robust Optimization Against Data Poisoning
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta +1
The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast…
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…
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…
Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons
Julien Fageot, Sadegh Farhadkhani, Lê Nguyên Hoang +1
Many applications, e.g. in content recommendation, sports, or recruitment, leverage the comparisons of alternatives to score those alternatives. The classical Bradley-Terry model a…
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 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…
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums
Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta +2
Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such a…
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang +1
To study the resilience of distributed learning, the "Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Wh…
Strategyproof Learning: Building Trustworthy User-Generated Datasets
Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang
We prove in this paper that, perhaps surprisingly, incentivizing data misreporting is not a fatality. By leveraging a careful design of the loss function, we propose Licchavi, a gl…
On the Strategyproofness of the Geometric Median
El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui +1
The geometric median, an instrumental component of the secure machine learning toolbox, is known to be effective when robustly aggregating models (or gradients), gathered from pote…
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)
El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui +3
We study Byzantine collaborative learning, where nodes seek to collectively learn from each others' local data. The data distribution may vary from one node to another. No node…