We consider nonconvex stochastic optimization problems in the asynchronous centralized distributed setup where the communication times from workers to a server can not be ignored, and the computation and communication times are potentially different for all workers.

On one hand, the local data of users may contain sensitive information, and privacy-preserving mechanisms are needed.

Effective communication between the server and workers plays a key role in distributed optimization. In this paper, we focus on optimizing communication, uncovering inefficiencies in prevalent downlink compression approaches.

Let M denote M = 2 1 1 2 1 . . . . . . . . . 1 2 1 1 2 R

These theoretical findings are supported by experimental results.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/