用联邦迁移学习动态优化电力转换器热模型，兼顾安全与适应性。
An Adaptive ML Framework for Power Converter Monitoring via Federated Transfer Learning
- 分段融合迁移与联邦学习，逐步适配多场景热模型。
- 微调法精度最高，适合实际部署；联邦聚合提升跨设备性能。
- 本地联邦适合数据难汇聚场景，云端联邦更易扩展。
本研究探索了将迁移学习（TL）与联邦学习（FL）结合的分段式框架，以适应电力转换器的热机器学习（ML）模型。该方法应对了运行条件差异、数据共享受限和安全问题。框架从基础模型出发，由多个客户端通过微调、迁移成分分析（TCA）和深度域自适应（DDA）三种先进域适应技术逐步优化。采用Flower框架进行联邦学习，使用联邦平均（Federated Averaging）聚合。基于现场数据验证表明，微调法具有高精度且实现简单，适用于实际应用。基准测试揭示了各方法在不同场景下的优劣。本地部署的联邦学习在无法聚合数据时表现更优，而随着客户端数量显著增加，云端联邦学习更具可扩展性和实用性。
原文摘要 · Abstract (English)
This study explores alternative framework configurations for adapting thermal machine learning (ML) models for power converters by combining transfer learning (TL) and federated learning (FL) in a piecewise manner. This approach inherently addresses challenges such as varying operating conditions, data sharing limitations, and security implications. The framework starts with a base model that is incrementally adapted by multiple clients via adapting three state-of-the-art domain adaptation techniques: Fine-tuning, Transfer Component Analysis (TCA), and Deep Domain Adaptation (DDA). The Flower framework is employed for FL, using Federated Averaging for aggregation. Validation with field data demonstrates that fine-tuning offers a straightforward TL approach with high accuracy, making it suitable for practical applications. Benchmarking results reveal a comprehensive comparison of these methods, showcasing their respective strengths and weaknesses when applied in different scenarios. Locally hosted FL enhances performance when data aggregation is not feasible, while cloud-based FL becomes more practical with a significant increase in the number of clients, addressing scalability and connectivity challenges.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。