Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging

Understand

Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets.

• Standard federated learning techniques are vulnerable to Byzantine failures, biased local datasets, and poisoning attacks.
• In this paper we introduce Adaptive Federated Averaging, a novel algorithm for robust federated learning that is designed to detect failures, attacks, and bad updates provided by participants in a collaborative model.
• We propose a Hidden Markov Model to model and learn the quality of model updates provided by each participant during training.