We consider the problem where nclients transmit d-dimensional real-valued vectors using dp1 `op1qqbits 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.

Such compression problems naturally arise in distributed and federated learning. We provide novel mathematical results and derivecomputationally efficient algorithms thataremore accurate than previous compression techniques.

Such compression problems naturally arise in distributed and federated learning.