  The decentralized nature of federated learning, that often leverages the
power of edge devices, makes it vulnerable to attacks against privacy and
security. The privacy risk for a peer is that the model update she computes on
her private data may, when sent to the model manager, leak information on those
private data. Even more obvious are security attacks, whereby one or several
malicious peers return wrong model updates in order to disrupt the learning
process and lead to a wrong model being learned. In this paper we build a
federated learning framework that offers privacy to the participating peers as
well as security against Byzantine and poisoning attacks. Our framework
consists of several protocols that provide strong privacy to the participating
peers via unlinkable anonymity and that are rationally sustainable based on the
co-utility property. In other words, no rational party is interested in
deviating from the proposed protocols. We leverage the notion of co-utility to
build a decentralized co-utile reputation management system that provides
incentives for parties to adhere to the protocols. Unlike privacy protection
via differential privacy, our approach preserves the values of model updates
and hence the accuracy of plain federated learning; unlike privacy protection
via update aggregation, our approach preserves the ability to detect bad model
updates while substantially reducing the computational overhead compared to
methods based on homomorphic encryption.
