On the Relevance of Byzantine Robust Optimization Against Data Poisoning

This research examines Byzantine robust optimization, a technique that protects machine learning systems when data is poisoned (corrupted or maliciously altered) and some workers (computers processing parts of the dataset) behave unpredictably in distributed networks. The study proves that Byzantine-robust approaches provide optimal protection even when facing weaker threats where only local datasets are poisoned, and shows that having some workers with completely corrupted data is more damaging than having workers with partially corrupted data.