On the Relevance of Byzantine Robust Optimization Against Data Poisoning

arXiv:2405.00491v1 Announce Type: new
Abstract: The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast networks of computing devices (also called {\em workers}). Beyond accuracy, the use of ML in critical domains such as healthcare and autonomous driving calls for robustness against {\em data poisoning}and some {\em faulty workers}. The problem of {\em Byzantine ML} formalizes these robustness issues by considering a distributed …

abstract accuracy arxiv availability beyond computing cs.lg data data poisoning devices domains machine machine learning networks optimization robust success type vast workers