# FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments

arXiv: https://arxiv.org/abs/2410.01386 · HTML: https://mlsanity.com/paper/2410.01386/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 2 Oct 2024.

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

- [MNIST-C: A Robustness Benchmark for Computer Vision](https://mlsanity.com/paper/1906.02337/) (2019)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Reactive Soft Prototype Computing for Concept Drift Streams](https://mlsanity.com/paper/2007.05432/) (2020)
- [Concept drift detection and adaptation for federated and continual learning](https://mlsanity.com/paper/2105.13309/) (2021)
- [FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments](https://mlsanity.com/paper/2305.08504/) (2023)

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

- [AI-Native Multi-Access Future Networks -- The REASON Architecture](https://mlsanity.com/paper/2411.06870/) (2024)
- [Towards the efficacy of federated prediction for epidemics on networks](https://mlsanity.com/paper/2412.02161/) (2024)

## Abstract (arXiv)

This paper presents Federated Learning with Adaptive Monitoring and Elimination (FLAME), a novel solution capable of detecting and mitigating concept drift in Federated Learning (FL) Internet of Things (IoT) environments. Concept drift poses significant challenges for FL models deployed in dynamic and real-world settings. FLAME leverages an FL architecture, considers a real-world FL pipeline, and proves capable of maintaining model performance and accuracy while addressing bandwidth and privacy constraints. Introducing various features and extensions on previous works, FLAME offers a robust solution to concept drift, significantly reducing computational load and communication overhead. Compared to well-known lightweight mitigation methods, FLAME demonstrates superior performance in maintaining high F1 scores and reducing resource utilisation in large-scale IoT deployments, making it a promising approach for real-world applications.