  In this work, we carry out the first, in-depth, privacy analysis of
Decentralized Learning -- a collaborative machine learning framework aimed at
addressing the main limitations of federated learning. We introduce a suite of
novel attacks for both passive and active decentralized adversaries. We
demonstrate that, contrary to what is claimed by decentralized learning
proposers, decentralized learning does not offer any security advantage over
federated learning. Rather, it increases the attack surface enabling any user
in the system to perform privacy attacks such as gradient inversion, and even
gain full control over honest users' local model. We also show that, given the
state of the art in protections, privacy-preserving configurations of
decentralized learning require fully connected networks, losing any practical
advantage over the federated setup and therefore completely defeating the
objective of the decentralized approach.
