  Federated learning enables a global machine learning model to be trained
collaboratively by distributed, mutually non-trusting learning agents who
desire to maintain the privacy of their training data and their hardware. A
global model is distributed to clients, who perform training, and submit their
newly-trained model to be aggregated into a superior model. However, federated
learning systems are vulnerable to interference from malicious learning agents
who may desire to prevent training or induce targeted misclassification in the
resulting global model. A class of Byzantine-tolerant aggregation algorithms
has emerged, offering varying degrees of robustness against these attacks,
often with the caveat that the number of attackers is bounded by some quantity
known prior to training. This paper presents Simeon: a novel approach to
aggregation that applies a reputation-based iterative filtering technique to
achieve robustness even in the presence of attackers who can exhibit arbitrary
behaviour. We compare Simeon to state-of-the-art aggregation techniques and
find that Simeon achieves comparable or superior robustness to a variety of
attacks. Notably, we show that Simeon is tolerant to sybil attacks, where other
algorithms are not, presenting a key advantage of our approach.
