# Efficient Client Selection in Federated Learning

arXiv: https://arxiv.org/abs/2502.00036 · HTML: https://mlsanity.com/paper/2502.00036/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset](https://mlsanity.com/paper/2012.14600/) (2020)
- [Network Anomaly Detection Using Federated Learning](https://mlsanity.com/paper/2303.07452/) (2023)

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

- [Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review](https://mlsanity.com/paper/2506.02887/) (2025)
- [EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning](https://mlsanity.com/paper/2606.07702/) (2026)

## Abstract (arXiv)

Federated Learning (FL) enables decentralized machine learning while preserving data privacy. This paper proposes a novel client selection framework that integrates differential privacy and fault tolerance. The adaptive client selection adjusts the number of clients based on performance and system constraints, with noise added to protect privacy. Evaluated on the UNSW-NB15 and ROAD datasets for network anomaly detection, the method improves accuracy by 7% and reduces training time by 25% compared to baselines. Fault tolerance enhances robustness with minimal performance trade-offs.