  In this paper, we design Top-DP, a novel solution to optimize the
differential privacy protection of decentralized image classification systems.
The key insight of our solution is to leverage the unique features of
decentralized communication topologies to reduce the noise scale and improve
the model usability. (1) We enhance the DP-SGD algorithm with this
topology-aware noise reduction strategy, and integrate the time-aware noise
decay technique. (2) We design two novel learning protocols (synchronous and
asynchronous) to protect systems with different network connectivities and
topologies. We formally analyze and prove the DP requirement of our proposed
solutions. Experimental evaluations demonstrate that our solution achieves a
better trade-off between usability and privacy than prior works. To the best of
our knowledge, this is the first DP optimization work from the perspective of
network topologies.
