We present new techniques (Basis Learning) how to make Newton-type methods applicable to Federated Learning. The idea is based on choosing appropriate basis for local Hessians representations which can lead to much better compression mechanism. Particularly, if we choose standard basis in the space of square matrices, we cover FedNL method.

I have finally arrived to Paris. Here I am going to study at Institut Polytechnique de Paris supervised by Olivier Fercoq. My first year will be based on Applied Mathematics and Statistics Program.

Our FedNL paper has been accepted to ICML workshops: International Workshop on Federated Learning for User Privacy and Data Confidentiality and Beyond first-order methods in ML systems. The paper will be presented as a poster at the first workshop and as a prerecorded video at the second one.

I am attending PRAIRE/MIAI Artificial Intelligence Summer School (PAISS 2021) (this year virtually only). This summer school is organized by PRAIRIE, MIAI, NAVER LABS Europe, and INRIA. The summer school comprises lectures conducted by renowned experts in different areas of artificial intelligence. As a part of the summer school I have presented the poster on our FedNL paper.

I have given a talk based on our paper Distributed Second Order Methods with Fast Rates and Compressed Communication at Maths&AI: MIPT-UGA young researchers workshop. The recorded video is available on YouTube. Here you can find the slides that I prepared for the talk.