# Sparse Uncertainty-Informed Sampling from Federated Streaming Data

arXiv: https://arxiv.org/abs/2408.17108 · HTML: https://mlsanity.com/paper/2408.17108/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Streaming Active Learning with Deep Neural Networks](https://mlsanity.com/paper/2303.02535/) (2023)
- [Federated Learning for Data Streams](https://mlsanity.com/paper/2301.01542/) (2023)

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

- [Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data](https://mlsanity.com/paper/2409.12575/) (2024)
- [Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation](https://mlsanity.com/paper/2507.10160/) (2025)

## Abstract (arXiv)

We present a numerically robust, computationally efficient approach for non-I.I.D. data stream sampling in federated client systems, where resources are limited and labeled data for local model adaptation is sparse and expensive. The proposed method identifies relevant stream observations to optimize the underlying client model, given a local labeling budget, and performs instantaneous labeling decisions without relying on any memory buffering strategies. Our experiments show enhanced training batch diversity and an improved numerical robustness of the proposal compared to existing strategies over large-scale data streams, making our approach an effective and convenient solution in FL environments.