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

• URL: http://arxiv.org/abs/2410.01386v2
• Date: Mon, 7 Oct 2024 14:14:39 GMT
• Title: FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments
• Authors: Ioannis Mavromatis, Stefano De Feo, Aftab Khan,
• Abstract summary: This paper presents Federated Learning with Adaptive Monitoring and Elimination (FLAME) FLAME is a novel solution capable of detecting and mitigating concept drift in Federated Learning (FL) Internet of Things (IoT) environments.
• Score: 2.553456266022126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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