Data Poisoning Detection in Federated Learning

Abstract

Federated Learning (FL) is an emerging machine learning paradigm in which multiple clients collaboratively train a model without exposing their local datasets. Under this paradigm, numerous clients share the responsibility of model training instead of having a centralized server. However, this enables clients of an FL system to send malicious model updates. An adversary could, e.g., train the local model with incorrect data to insert an adversary-defined objective into the model or cause a severe drop in accuracy.

Topics & Concepts