An Adaptive ML Framework for Power Converter Monitoring via Federated Transfer Learning

• URL: http://arxiv.org/abs/2504.16866v1
• Date: Wed, 23 Apr 2025 16:39:54 GMT
• Title: An Adaptive ML Framework for Power Converter Monitoring via Federated Transfer Learning
• Authors: Panagiotis Kakosimos, Alireza Nemat Saberi, Luca Peretti,
• Abstract summary: This study explores alternative framework configurations for adapting thermal machine learning (ML) models for power converters.<n>The framework starts with a base model that is incrementally adapted by multiple clients via adapting three state-of-the-art domain adaptation techniques.<n> Validation with field data demonstrates that fine-tuning offers a straightforward TL approach with high accuracy, making it suitable for practical applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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