Additional Insights on the paper “Federated Learning is Better with Non-Homomorphic Encryption” Published on 05 December 2023

The paper Unlocking FedNL: Self-Contained Compute-Optimized Implementation has been published on arXiv: arXiv:2410.08760.

Paper PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression has been accepted at NeurIPS 2024.

Paper “Don’t Compress Gradients in Random Reshuffling: Compress Gradient Differences” has been accepted at NeurIPS 2024.

Unlocking FedNL Self-Contained Compute-Optimized Implementation (Research from KAUST) in Virtual Radio Studio

Internship in Private Federated Learning ML Team Apple Inc.

The new paper PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression with a new fine-tuning technique for highly-compressed LLMs.

Remote presentation of our latest research for Apple.

Remote presentation of our latest research for NVIDIA.

This technical note is devoted to covering information regarding all primary C++ programming language standards: C++03/98/11/14/17/20/23.

“Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants” has been accepted and will be presented at ICLR 2024.

“Unlocking FedNL: Self-Contained Compute-Optimized Implementation” will be presented at KAUST Rising Stars AI Symposium 2024.

The paper Personalized Federated Learning with Communication Compression was accepted by the Transactions on Machine Learning Research.

“Federated Learning is Better with Non-Homomorphic Encryption” at the 4th International Workshop on Distributed Machine Learning(DistributedML 2023).

The paper Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling was accepted by the Transactions on Machine Learning Research.

The paper Faster Rates for Compressed Federated Learning with Client-Variance Reduction was accepted by the SIAM Journal (SIMODS).

Receiving the Dean’s List Award 2023.

Technical Note. Exploring the Python 3 Language from a Computing Perspective.

Update of technical note devoted to covering information regarding all primary C++ programming language standards: C++98/03/11/14/17/20.

Our two papers on Federated Learning have been accepted to FL-ICML-2023.

AMD Radeon Instinct MI50 from AMD Inc.

The new paper Error Feedback Shines when Features are Rare has been released.

“FL_PyTorch: Optimization Research Simulator for Federated Learning” at SIAM Conference on Optimization 2023.

My old website with 151 notes was deleted on 10 Mar. 2023, 08:58 by a “Google” user.

FedNL. Practical Implementation (Ongoing project) at VCC OPEN HOUSE 2023 event.

The new paper Federated Learning with Regularized Client Participation has been released.

About Courses that I have audited during Fall, 2022: CS294V and AMCS 343.

Today on Monday 24 October 2022 I defended my CS Ph.D. Proposal and I have achieved the Status of a Ph.D. candidacy.

Second Place in the KAUST Chess Tournament.

The new paper Personalized Federated Learning with Communication Compression has been published.

Formal affiliation with the SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence (SDAIA-KAUST AI).

This technical note is devoted to covering information regarding all primary C++ programming language standards: C++03/98/11/14/17/20.

The paper MARINA: Faster Non-Convex Distributed Learning with Compression on Principles of Distributed Learning of ACM PODC 2022.

FL_PyTorch: Optimization Research Simulator for Federated Learning is publicly available on GitHub.

Research Scientist Intern (AI) offer, Meta/Facebook Inc., USA, Menlo Park.

The new paper Federated Optimization Algorithms with Random Reshuffling and Gradient Compression has been outed.

The new paper Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling has been outed.

“FL_PyTorch: Optimization Research Simulator for Federated Learning” will be presented in Rising Stars in AI Symposium 2022.

The new paper Faster Rates for Compressed Federated Learning with Client-Variance Reduction has been outed.

About advanced course “6-172. Performance Engineering of Software Systems.” at MIT with summarizing useful materials from that course for anybody who wants to optimize binary code performance.

Systematized catalog of courses at Stanford relative to AI, Machine Learning, Optimization, Statistics, Control Theory, Computer Vision, and Natural Language Processing.

Systematized catalog of courses at Stanford relative to creating various systems by leveraging science and engineering from various fields.

“Differential Privacy” and “Efficient Deep Learning” classes at KAUST.

«FL_PyTorch: Optimization Research Simulator for Federated Learning» has been accepted to DistributedML 2021.

Several engaging summer schools that I have a chance to attend during the summer of 2021.

My second semester (Spring, 2021) at KAUST CEMSE/CS.

“MARINA: Faster Non-Convex Distributed Learning with Compression” has been accepted to ICML 2021.

About final project at King Abdullah University of Science and Technology for the course CS 380 GPU and GPGPU Programming, 2020, Fall.

Poster for MARINA Paper presented at Poster session during Efficient Distributed Optimization workshop.

Post about the paper https://arxiv.org/abs/2102.07845.

Post about my Open-Source KD tree implementation.

The topic of my research at KAUST in 90 seconds.

About my first semester (Fall, 2020) at KAUST.

I have joined the KAUST Ph.D. Computer Science program at CEMSE and prof. Peter Richtarik Optimization and Machine Learning Lab.

Between 2011 to 2020 I used another homepage https://sites.google.com/site/burlachenkok/.