# Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection

arXiv: https://arxiv.org/abs/2503.15448 · HTML: https://mlsanity.com/paper/2503.15448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Asynchronous Federated Learning on Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2109.04269/) (2021)
- [A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset](https://mlsanity.com/paper/2012.14600/) (2020)
- [dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training](https://mlsanity.com/paper/2205.02473/) (2022)
- [Network Anomaly Detection Using Federated Learning](https://mlsanity.com/paper/2303.07452/) (2023)

## Abstract (arXiv)

Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems, where diverse client configurations and network conditions impact efficiency and detection accuracy. Existing approaches attempt optimization individually but struggle to balance reduced overhead with performance. This paper presents an adaptive FL framework combining batch size optimization, client selection, and asynchronous updates for efficient anomaly detection. Using UNSW-NB15 for general network traffic and ROAD for automotive networks, our framework reduces communication overhead by 97.6% (700.0s to 16.8s) while maintaining comparable accuracy (95.10% vs. 95.12%). The Mann-Whitney U test confirms significant improvements (p < 0.05). Profiling analysis reveals efficiency gains via reduced GPU operations and memory transfers, ensuring robust detection across varying client conditions.