Belt and Braces: When Federated Learning Meets Differential Privacy

With the development of advanced algorithms, computing capabilities, and available datasets, machine learning (ML) has been widely adopted to solve real-world problems in various application domains. The success of ML often relies on large amounts of application-specified training data, especially for large models such as ChatGPT. However, this data is often generated and scattered among enormous network edges or users' end devices, and can be quite sensitive and impractical to be moved to a central location as the result of regulatory laws (for example, GDPR) or privacy concerns.8 This fact has brought an inconvenient dilemma between large-scale ML and increasingly severe data isolation. The conflict between data hungriness and privacy awareness is becoming increasingly prominent in the artificial intelligence (AI) era.