Personalised federated learning with differential privacy and gradient selection

• Main Authors: Lee, Jason Zhi Xin, Ng, Kai Chin, Toh, Arnold Xuan Ming
• Other Authors: Lam Kwok Yan
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Computer science and engineering::Data
• Online Access: https://hdl.handle.net/10356/151517
• Institution: Nanyang Technological University

The fast-emerging field of federated learning holds the promise of allowing clients to contribute to a central machine learning model without the need to send their data to a central server, thus providing privacy for their data. Two issues arise: dealing with statistical heterogeneity in datasets, which is often the case in real-world settings, and obtaining stricter data privacy through employing privacy-preserving mechanisms. In this paper, we use personalized layers in a federated Convolutional Neural Network model to address statistical heterogeneity and use differential privacy to provide mathematically rigorous privacy guarantees for the federated learning model. We also propose a gradient selection technique to increase model performance. We developed a framework combining these techniques and experimentally demonstrate the effectiveness of the proposed framework on a dataset in improving model performance whilst maintaining a reasonable level of privacy guarantee and training efficiency.