Paper:
Communication Complexity of Distributed Convex Learning and Optimization
We study the fundamental limits to communication-efficient distributed methods for convex learning and optimization, under different assumptions on the information available to individual machines, and the types of functions considered. We identify cases where existing algorithms are already worst-case optimal, as well as cases where room for further improvement is still possible. Among other things, our results indicate that without similarity between the local objective functions (due to statistical data similarity or otherwise) many communication rounds may be required, even if the machines have unbounded computational power.

Review:
The paper gives lower bounds for the number of rounds needed for optimizing convex functions distributedly. In the main result of the paper (Theorem 1), it is assumed that we are optimizing a sum of delta-related and \lambda functions with a certain class of algorithms (which can basically take gradient, use local hessein to do preconditioning, and other related operations). It is shown that number of rounds for this task is at least \sqrt{\detla/\lambda}. These are matched by DiSCO under quadratic case. The class of functions concerned here contains the (probably the most) interesting situation, random partition of a set of functions, that people care about, though the former is a strict superset of the later. The paper also shows lower bounds for the case when the local functions can be arbitrary, and the case when only one round of communication is allowed. The main proof technique is similar in spirit to the query complexity of first order method. Essentially the constructed hard instances is such that starting from initial iterate 0, every round of communication can only provide information for one additional dimension and therefore without enough rounds, the iterate can only be in the span of the first several dimensions and dooms to have large error. In summary, the paper has nice results and is well written. I wouldn't say that results are very strong due to the restrictions to the algorithms and the large class of functions concerned. However, proving lower bounds for rounds of communications are typicallyhard (e.g. for the typical boolean function and two player case), and therefore restriction to a class of algorithms should be not be a problem. Finally, there are some minor issues that I hope the author could clarify/revise/strengthen: 1. the argument seems to depend on the initial point w to be 0 heavily. Is it possible to allow a random starting point (which doesn't depend on the function)? I don't see an easy fix for it. (Maybe previous proofs for first order methods (like Nemirovsky-Yudin) suffers from the same drawback? If so maybe it's good to clarify) 2. it seems to be possible to extend the lower bound to the query model in the flavor of increment first-order algorithm like in Nemirovsky-Yudin or Agarwal-Bottou? It would be nice if one can allow arbitrary update based on the iterate, gradient, preconditioned gradient. 3. It is mentioned in line 86 that "we conjecture that the optimal algorithm in this case is again .. ". I got confused about why this is a conjecture? Maybe it is good to spell out the gap more explicitly.