  Collaborative machine learning (ML) is widely used to enable institutions to
learn better models from distributed data. While collaborative approaches to
learning intuitively protect user data, they remain vulnerable to either the
server, the clients, or both, deviating from the protocol. Indeed, because the
protocol is asymmetric, a malicious server can abuse its power to reconstruct
client data points. Conversely, malicious clients can corrupt learning with
malicious updates. Thus, both clients and servers require a guarantee when the
other cannot be trusted to fully cooperate. In this work, we propose a
peer-to-peer (P2P) learning scheme that is secure against malicious servers and
robust to malicious clients. Our core contribution is a generic framework that
transforms any (compatible) algorithm for robust aggregation of model updates
to the setting where servers and clients can act maliciously. Finally, we
demonstrate the computational efficiency of our approach even with 1-million
parameter models trained by 100s of peers on standard datasets.
