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DRIVE: One-bit Distributed Mean Estimation
Shay Vargaftik · Ran Ben-Basat · Amit Portnoy · Gal Mendelson · Yaniv Ben-Itzhak · Michael Mitzenmacher

Thu Dec 09 08:30 AM -- 10:00 AM (PST) @ Virtual
We consider the problem where $n$ clients transmit $d$-dimensional real-valued vectors using $d(1+o(1))$ bits each, in a manner that allows the receiver to approximately reconstruct their mean. Such compression problems naturally arise in distributed and federated learning. We provide novel mathematical results and derive computationally efficient algorithms that are more accurate than previous compression techniques. We evaluate our methods on a collection of distributed and federated learning tasks, using a variety of datasets, and show a consistent improvement over the state of the art.

Author Information

Shay Vargaftik (VMware Research)
Ran Ben-Basat (University College London)
Amit Portnoy (Ben-Gurion University of the Negev)
Gal Mendelson (Stanford)
Yaniv Ben-Itzhak (VMware Research)
Michael Mitzenmacher (Harvard University)

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