Federated Machine Learning Architectures for Image Classification

Sammanfattning

In this thesis, we explore a new method for binary image classification of semiconductorcomponents using federated learning at Mycronic AB, enabling model training on Pick andPlace (PnP) machines without centralizing sensitive data. Initially, we set a baseline bychoosing a suitable Convolutional Neural Network (CNN) architecture, implementing datapreprocessing methods, and optimizing various hyperparameters. We then assess variousfederated learning algorithms to manage the inherent statistical heterogeneity in distributeddatasets. Our approach is validated using a real-world dataset annotated by Mycronic,confirming that our findings are applicable to real industrial scenarios.