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Found 9 papers, 2 papers with code
Robustness, Efficiency, or Privacy: Pick Two in Machine Learning
The success of machine learning (ML) applications relies on vast datasets and distributed architectures which, as they grow, present major challenges.
SABLE: Secure And Byzantine robust LEarning
SABLE leverages HTS, a novel and efficient homomorphic operator implementing the prominent coordinate-wise trimmed mean robust aggregator.
On the Privacy-Robustness-Utility Trilemma in Distributed Learning
The latter amortizes the dependence on the dimension in the error (caused by adversarial workers and DP), while being agnostic to the statistical properties of the data.
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity
Byzantine machine learning (ML) aims to ensure the resilience of distributed learning algorithms to misbehaving (or Byzantine) machines.
On the Impossible Safety of Large AI Models
Large AI Models (LAIMs), of which large language models are the most prominent recent example, showcase some impressive performance.
Robust Collaborative Learning with Linear Gradient Overhead
We present MoNNA, a new algorithm that (a) is provably robust under standard assumptions and (b) has a gradient computation overhead that is linear in the fraction of faulty machines, which is conjectured to be tight.
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums
We present \emph{RESAM (RESilient Averaging of Momentums)}, a unified framework that makes it simple to establish optimal Byzantine resilience, relying only on standard machine learning assumptions.
Combining Differential Privacy and Byzantine Resilience in Distributed SGD
Privacy and Byzantine resilience (BR) are two crucial requirements of modern-day distributed machine learning.
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML).
