This study explores alternative framework configurations for adapting thermal machine learning 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, and deep domain adaptation. 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, whereas cloud-based FL becomes more practical with a significant increase in the number of clients, addressing scalability and connectivity challenges.