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.

翻译：本文提出了一种具备自适应监测与消除能力的联邦学习方法（FLAME），该创新方案能够检测并缓解联邦学习物联网环境中的概念漂移问题。在动态现实场景中部署的联邦学习模型面临概念漂移带来的严峻挑战。FLAME基于联邦学习架构，结合真实场景下的联邦学习流程，在满足带宽与隐私约束的同时，被证明能够有效维持模型性能与精度。通过在现有研究基础上引入多样化特性与扩展机制，FLAME为概念漂移问题提供了鲁棒的解决方案，显著降低了计算负载与通信开销。与当前主流的轻量化缓解方法相比，FLAME在大规模物联网部署中展现出更优异的性能，既能维持较高的F1分数，又能降低资源消耗，为实际应用提供了具有前景的技术路径。