The process-defect relationship is one of the key elements to the certification of additive manufacturing (AM) parts, which has been a major challenge in accelerating AM technology deployments in the industry. Advanced machine learning methods that leverage massive data to characterize the process-defect relationship have been studied for AM certifications. However, some AM fabrications and certification courses, especially for high-valued metallic parts, are lengthy and costly; thus, if the certification could be transferrable between different AM systems, it may greatly broaden the industrial use of AM technologies. Though feasible in theory, combining data from multiple AM systems on a shared platform for the certification purpose is not practical because of the desire to protect intellectual properties and sensitive data. What is lacking, therefore, is a holistic strategy to share knowledge learned from different AM systems without compromising the private information. This Faculty Early Career Development (CAREER) award supports fundamental research on privacy-preserving AM process-defect modeling and certification means across different systems. The project aims to establish a transfer learning groundwork, while protecting the process and part confidentiality, to understand and establish the process-defect relationship in metal AM between different systems. In addition, educational activities closely integrated with the research will provide basic training in privacy-pr