Implementing a Network Optimized Federated Learning Method From the Ground up

Sammanfattning: This bachelor thesis presents the implementation ofa simple fully connected neural network (FCNN) and federatedneural network with stochastic quantization from scratch andcompares their performance. Federated learning enables multipleparties to contribute to a machine learning model withoutsharing their sensitive data. The federated learning approach isbecoming increasingly popular due to its ability to train modelson decentralized data sources while maintaining privacy andsecurity. Both the FCNN and federated network are trainedand tested on the Modified National Institute of Standards andTechnology (MNIST) database, the first one achieving around90% accuracy after 50 epochs while the federated architectureonly able to reach around 45% accuracy. This remains the samewhen data is quantized.