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Bio: Konstantin Mishchenko received his double-degree MSc from Paris-Dauphine and École normale supérieure Paris-Saclay in 2017. He did his PhD under the supervision of Peter Richtárik, and had research internships at Google Brain and Amazon. Konstantin has been recognized as an outstanding reviewer for NeurIPS19, ICML20, AAAI20, ICLR21, and ICML21. He has published 8 conference papers at ICML, NeurIPS, AISTATS, and UAI, 1 journal paper at SIOPT, 6 workshop papers, and co-authored 8 preprints, some of which are currently under peer review. In 2021, Konstantin is joining the group of Alexandre d’Aspremont and Francis Bach in Paris as a Postdoctoral Researcher.

Talk Title: Optimization for Federated Learning

Talk Abstract: Optimization has been a vital tool for enabling the success of machine learning. In the recently introduced paradigm of federated learning, where devices or organizations unite to train a model without revealing their private data, optimization has been particularly nontrivial. The peculiarities of federated learning that make it difficult include unprecedented privacy constraints, the difficulty of communication with a server, and high heterogeneity of the data across the participating parties. Nevertheless, the potential applications of federated learning, such as machine learning for health care, banking, and smartphones, have sparked global interest in the problem and quick growth in the number of publications.

In this talk, we will discuss some of the recent advances in optimization for federated learning. We will formulate the key challenges in communication efficiency and personalization and propose ways for tackling them that are motivated by theory. To this end, we will discuss the convergence properties of some existing and new federated learning algorithms that leverage on-device (local) iterations as a way to limit communication.