FLAME动态检测并缓解物联网联邦学习中的概念漂移。
FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments
- 基于自适应监测机制，实时识别模型性能退化。
- 在大规模物联网部署中保持高F1分数，降低资源消耗。
- 适合资源受限的实时物联网场景，兼顾隐私与效率。
本文提出一种新型联邦学习框架FLAME（Federated Learning with Adaptive Monitoring and Elimination），用于在动态、真实的物联网（IoT）环境中检测和缓解概念漂移。概念漂移会严重影响联邦学习模型在现实场景中的性能。FLAME采用端到端的联邦学习架构，构建真实世界的应用流程，在满足带宽与隐私约束的前提下，有效维持模型精度。通过引入多项改进与扩展，该方法显著降低计算负载与通信开销。相比现有轻量级缓解方法，FLAME在大规模物联网部署中表现出更优的性能，能够维持高F1分数并减少资源占用，具备良好的实际应用前景。
原文摘要 · Abstract (English)
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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