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Found 10 papers, 4 papers with code
On the Relevance of Byzantine Robust Optimization Against Data Poisoning
It has been argued that the seemingly weaker threat model where only workers' local datasets get poisoned is more reasonable.
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}.
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches.
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.
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
More specifically, we prove that every gradient attack can be reduced to data poisoning, in any personalized federated learning system with PAC guarantees (which we show are both desirable and realistic).
Strategyproof Learning: Building Trustworthy User-Generated Datasets
We prove in this paper that, perhaps surprisingly, incentivizing data misreporting is not a fatality.
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)
We study Byzantine collaborative learning, where $n$ nodes seek to collectively learn from each others' local data.
