Jakub Konečný

TL;DR: Two ways to reduce the uplink communication costs are proposed: structured updates, where the user directly learns an update from a restricted space parametrized using a smaller number of variables, e.g. either low-rank or a random mask; and sketched updates, which learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling.

TL;DR: In this paper, a scalable production system for federated learning in the domain of mobile devices, based on TensorFlow, is presented. Butler et al. describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.

TL;DR: A new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large number of nodes, is introduced, to train a high-quality centralized model.

TL;DR: Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

TL;DR: LEAF is proposed, a modular benchmarking framework for learning in federated settings that includes a suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments.