  Achieving communication efficiency in decentralized machine learning has been
attracting significant attention, with communication compression recognized as
an effective technique in algorithm design. This paper takes a first step to
understand the role of gradient clipping, a popular strategy in practice, in
decentralized nonconvex optimization with communication compression. We propose
PORTER, which considers two variants of gradient clipping added before or after
taking a mini-batch of stochastic gradients, where the former variant PORTER-DP
allows local differential privacy analysis with additional Gaussian
perturbation, and the latter variant PORTER-GC helps to stabilize training. We
develop a novel analysis framework that establishes their convergence
guarantees without assuming the stringent bounded gradient assumption. To the
best of our knowledge, our work provides the first convergence analysis for
decentralized nonconvex optimization with gradient clipping and communication
compression, highlighting the trade-offs between convergence rate, compression
ratio, network connectivity, and privacy.
