EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression

The starting point of this paper is the discovery of a novel and simple error-feedback mechanism, which we call EF21-P, for dealing with the error introduced by a contractive compressor. Unlike all prior works on error feedback, where compression and correction operate in the dual space of gradients, our mechanism operates in the primal space of models. While we believe that EF21-P may be of interest in many situations where it is often advantageous to perform model perturbation prior to the computation of the gradient (e.g., randomized smoothing and generalization), in this work we focus our attention on its use as a key building block in the design of communication-efficient distributed optimization methods supporting bidirectional compression. In particular, we employ EF21-P as the mechanism for compressing and subsequently error-correcting the model broadcast by the server to the workers. By combining EF21-P with suitable methods performing worker-to-server compression, we obtain novel methods supporting bidirectional compression and enjoying new state-of-the-art theoretical communication complexity for convex and nonconvex problems. For example, our bounds are the first that manage to decouple the variance/error coming from the workers-to-server and server-to-workers compression, transforming a multiplicative dependence to an additive one. In the convex regime, we obtain the first bounds that match the theoretical communication complexity of gradient descent. Even in this convex regime, our algorithms work with biased gradient estimators, which is non-standard and requires new proof techniques that may be of independent interest. Finally, our theoretical results are corroborated through suitable experiments.