# Concept Drift Detection in Federated Networked Systems

arXiv: https://arxiv.org/abs/2109.06088 · HTML: https://mlsanity.com/paper/2109.06088/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2006.01412/) (2020)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [Concept Drift Adaptation by Exploiting Historical Knowledge](https://mlsanity.com/paper/1702.03500/) (2017)
- [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021)
- [Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems](https://mlsanity.com/paper/2102.10142/) (2021)

## Built on by (most cited first, 10 of 10)

- [Non-IID data and Continual Learning processes in Federated Learning: A long road ahead](https://mlsanity.com/paper/2111.13394/) (2021)
- [Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2206.00799/) (2022)
- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case](https://mlsanity.com/paper/2205.02706/) (2022)
- [An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization](https://mlsanity.com/paper/2209.10428/) (2022)
- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [A Model Drift Detection and Adaptation Framework for 5G Core Networks](https://mlsanity.com/paper/2209.06852/) (2022)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [Thwarting Cybersecurity Attacks with Explainable Concept Drift](https://mlsanity.com/paper/2403.13023/) (2024)
- [FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning](https://mlsanity.com/paper/2511.06797/) (2025)

## Abstract (arXiv)

As next-generation networks materialize, increasing levels of intelligence are required. Federated Learning has been identified as a key enabling technology of intelligent and distributed networks; however, it is prone to concept drift as with any machine learning application. Concept drift directly affects the model's performance and can result in severe consequences considering the critical and emergency services provided by modern networks. To mitigate the adverse effects of drift, this paper proposes a concept drift detection system leveraging the federated learning updates provided at each iteration of the federated training process. Using dimensionality reduction and clustering techniques, a framework that isolates the system's drifted nodes is presented through experiments using an Intelligent Transportation System as a use case. The presented work demonstrates that the proposed framework is able to detect drifted nodes in a variety of non-iid scenarios at different stages of drift and different levels of system exposure.