# FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning

arXiv: https://arxiv.org/abs/2412.14226 · HTML: https://mlsanity.com/paper/2412.14226/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning](https://mlsanity.com/paper/2105.05883/) (2021)
- [FedSampling: A Better Sampling Strategy for Federated Learning](https://mlsanity.com/paper/2306.14245/) (2023)

## Built on by (most cited first, 2 of 2)

- [Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations](https://mlsanity.com/paper/2604.12968/) (2026)
- [HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning](https://mlsanity.com/paper/2508.06692/) (2025)

## Abstract (arXiv)

Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how to sample participating clients in each round effectively and in a privacy-preserving manner. In this paper, we propose textitFedSTaS, a client and data-level sampling method inspired by textitFedSTS and textitFedSampling. In each federated learning round, textitFedSTaS stratifies clients based on their compressed gradients, re-allocate the number of clients to sample using an optimal Neyman allocation, and sample local data from each participating clients using a data uniform sampling strategy. Experiments on three datasets show that textitFedSTaS can achieve higher accuracy scores than those of textitFedSTS within a fixed number of training rounds.