555 citations · 585 across the 9 of their papers we have counts for
5 papers · 1 filter
Combining Differential Privacy and Byzantine Resilience in Distributed SGD
Rachid Guerraoui, Nirupam Gupta, Rafael Pinot +2
Privacy and Byzantine resilience (BR) are two crucial requirements of modern-day distributed machine learning. The two concepts have been extensively studied individually but the q…
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot +2
This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Sto…
Garfield: System Support for Byzantine Machine Learning
Rachid Guerraoui, Arsany Guirguis, Jérémy Max Plassmann +2
We present Garfield, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzant…
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…
Distributed Momentum for Byzantine-resilient Learning
El-Mahdi El-Mhamdi, Rachid Guerraoui, Sébastien Rouault
Momentum is a variant of gradient descent that has been proposed for its benefits on convergence. In a distributed setting, momentum can be implemented either at the server or the…