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Found 5 papers, 1 papers with code
The Privacy Power of Correlated Noise in Decentralized Learning
Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data.
Tackling Byzantine Clients in Federated Learning
The natural approach to robustify FL against adversarial clients is to replace the simple averaging operation at the server in the standard $\mathsf{FedAvg}$ algorithm by a \emph{robust averaging rule}.
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.
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.
