ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression
Abstract
Federated sampling algorithms have recently gained great popularity in the community of machine learning and statistics. This paper proposes a new federated sampling algorithm called Error Feedback Langevin algorithms (ELF). In particular, we analyze the combinations of EF21 and EF21-P with the federated Langevin Monte-Carlo. We propose three algorithms, P-ELF, D-ELF, and B-ELF, that use primal, dual, and bidirectional compressors. We analyze the proposed methods under Log-Sobolev inequality and provide non-asymptotic convergence guarantees. Simple experimental results support our theoretical findings.
Cite
Text
Karagulyan and Richtárik. "ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression." Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, 2025.Markdown
[Karagulyan and Richtárik. "ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression." Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, 2025.](https://mlanthology.org/uai/2025/karagulyan2025uai-elf/)BibTeX
@inproceedings{karagulyan2025uai-elf,
title = {{ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression}},
author = {Karagulyan, Avetik and Richtárik, Peter},
booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence},
year = {2025},
pages = {1965-1989},
volume = {286},
url = {https://mlanthology.org/uai/2025/karagulyan2025uai-elf/}
}