  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. In contrast to existing robust federated learning schemes, we
propose a robust aggregation rule that detects and discards bad or malicious
local model updates at each training iteration. This includes a mechanism that
blocks unwanted participants, which also increases the computational and
communication efficiency. Our experimental evaluation on 4 real datasets show
that our algorithm is significantly more robust to faulty, noisy and malicious
participants, whilst being computationally more efficient than other
state-of-the-art robust federated learning methods such as Multi-KRUM and
coordinate-wise median.

Machine Learning;Distributed, Parallel, and Cluster Computing;Machine Learning