# One-Point Feedback for Composite Optimization with Applications to Distributed and Federated Learning

arXiv: https://arxiv.org/abs/2107.05951 · HTML: https://mlsanity.com/paper/2107.05951/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis](https://mlsanity.com/paper/2304.07504/) (2023)

## Abstract (arXiv)

This work is devoted to solving the composite optimization problem with the mixture oracle: for the smooth part of the problem, we have access to the gradient, and for the non-smooth part, only the one-point zero-order oracle is available. For such a setup, we present a new method based on the sliding algorithm. Our method allows to separate the oracle complexities and to compute the gradient for one of the functions as rarely as possible. The paper also presents the applicability of our new method to the problems of distributed optimization and federated learning. Experimental results confirm the theory.