  This paper introduces ZIP-DL, a novel privacy-aware decentralized learning
(DL) algorithm that exploits correlated noise to provide strong privacy
protection against a local adversary while yielding efficient convergence
guarantees for a low communication cost. The progressive neutralization of the
added noise during the distributed aggregation process results in ZIP-DL
fostering a high model accuracy under privacy guarantees. ZIP-DL further uses a
single communication round between each gradient descent, thus minimizing
communication overhead. We provide theoretical guarantees for both convergence
speed and privacy guarantees, thereby making ZIP-DL applicable to practical
scenarios. Our extensive experimental study shows that ZIP-DL significantly
outperforms the state-of-the-art in terms of vulnerability/accuracy trade-off.
In particular, ZIP-DL (i) reduces the efficacy of linkability attacks by up to
52 percentage points compared to baseline DL, (ii) improves accuracy by up to
37 percent w.r.t. the state-of-the-art privacy-preserving mechanism operating
under the same threat model as ours, when configured to provide the same
protection against membership inference attacks, and (iii) reduces
communication by up to 10.5x against the same competitor for the same level of
protection.
