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This document summarizes a technical paper that proposes using federated learning to enable privacy-preserving collaborative machine learning for semantic segmentation and pixel-wise defect detection in additive manufacturing. Federated learning allows multiple parties to build a shared model by training locally on their own private datasets and sharing only model updates rather than raw data. The paper develops a federated learning approach using a U-Net architecture for semantic segmentation. Results show
- Author
- Mann Strong
- Language
- EN