  Traditional machine learning systems were designed in a centralized manner.
In such designs, the central entity maintains both the machine learning model
and the data used to adjust the model's parameters. As data centralization
yields privacy issues, Federated Learning was introduced to reduce data sharing
and have a central server coordinate the learning of multiple devices. While
Federated Learning is more decentralized, it still relies on a central entity
that may fail or be subject to attacks, provoking the failure of the whole
system. Then, Decentralized Federated Learning removes the need for a central
server entirely, letting participating processes handle the coordination of the
model construction. This distributed control urges studying the possibility of
malicious attacks by the participants themselves. While poisoning attacks on
Federated Learning have been extensively studied, their effects in
Decentralized Federated Learning did not get the same level of attention. Our
work is the first to propose a methodology to assess poisoning attacks in
Decentralized Federated Learning in both churn free and churn prone scenarios.
Furthermore, in order to evaluate our methodology on a case study
representative for gossip learning we extended the gossipy simulator with an
attack injector module.
