Attacks of fairness in Federated Learning
Joseph Rance Filip Svoboda
Federated Learning is an important emerging distributed training paradigm
that keeps data private on clients. It is now well understood that by
controlling only a small subset of FL clients, it is possible to introduce a
backdoor to a federated learning model, in the presence of certain attributes.
In this paper, we present a new type of attack that compromises the fairness of
the trained model. Fairness is understood to be the attribute-level performance
distribution of a trained model. It is particularly salient in domains where,
for example, skewed accuracy discrimination between subpopulations could have
disastrous consequences. We find that by employing a threat model similar to
that of a backdoor attack, an attacker is able to influence the aggregated
model to have an unfair performance distribution between any given set of
attributes. Furthermore, we find that this attack is possible by controlling
only a single client. While combating naturally induced unfairness in FL has
previously been discussed in depth, its artificially induced kind has been
neglected. We show that defending against attacks on fairness should be a
critical consideration in any situation where unfairness in a trained model
could benefit a user who participated in its training.