Implementation of Federated Learning on Raspberry Pi Boards : Implementation of Federated Learning on Raspberry Pi Boards with Paillier Encryption

Sammanfattning: The development of innovative applications of Artificial Intelligence (AI) is inseparable from the sharing of public data. However, as people strengthen their awareness of the protection of personal data privacy, it is more and more difficult to collect data from multiple data sources and there is also a risk of leakage in unified data management. But neural networks need a lot of data for model learning and analysis. Federated learning (FL) can solve the above difficulties. It allows the server to learn from the local data of multiple clients without collecting them. This thesis mainly deploys FL on the Raspberry Pi (RPi) and achieves federated averaging (FedAvg) as aggregation method. First in the simulation, we compare the difference between FL and centralized learning (CL). Then we build a reliable communication system based on socket on testbed and implement FL on those devices. In addition, the Paillier encryption algorithm is configured for the communication in FL to avoid model parameters being exposed to public network directly. In other words, the project builds a complete and secure FL system based on hardware.