Rafaël Pinot

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?

2021 · arXiv (Cornell University)

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 Stochastic Gradient Descent (SGD) learning algorithm is feasible with both …

Combining Differential Privacy and Byzantine Resilience in Distributed\n SGD

2021 · arXiv (Cornell University)

Privacy and Byzantine resilience (BR) are two crucial requirements of\nmodern-day distributed machine learning. The two concepts have been extensively\nstudied individually but the question of how to combine them effectively\nremains unanswered. This paper contributes to addressing …