  Establishing how a set of learners can provide privacy-preserving federated
learning in a fully decentralized (peer-to-peer, no coordinator) manner is an
open problem. We propose the first privacy-preserving consensus-based algorithm
for the distributed learners to achieve decentralized global model aggregation
in an environment of high mobility, where the communication graph between the
learners may vary between successive rounds of model aggregation. In
particular, in each round of global model aggregation, the Metropolis-Hastings
method is applied to update the weighted adjacency matrix based on the current
communication topology. In addition, the Shamir's secret sharing scheme is
integrated to facilitate privacy in reaching consensus of the global model. The
paper establishes the correctness and privacy properties of the proposed
algorithm. The computational efficiency is evaluated by a simulation built on a
federated learning framework with a real-word dataset.
