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Article summary:
1. Federated learning (FL) is a type of collaborative machine learning framework that can preserve private data from mobile terminals (MTs).
2. To protect user-level privacy, a user-level differential privacy (UDP) algorithm is proposed by adding artificial noise to the shared models before uploading them to servers.
3. A communication rounds discounting (CRD) method is proposed to achieve an optimal number of communication rounds and improve both the training efficiency and model quality for the given privacy protection levels.
Article analysis:
The article provides a comprehensive overview of federated learning (FL), its potential risks, and how to address them with user-level differential privacy (UDP). The authors provide a detailed analysis of their proposed UDP algorithm, including theoretical convergence upper-bound, optimal number of communication rounds, and communication rounds discounting (CRD) method. The article also includes extensive experiments to validate the effectiveness of their proposed methods.
The article appears to be reliable in terms of its content and methodology. The authors have provided sufficient evidence for their claims and have explored counterarguments where appropriate. There does not appear to be any promotional content or partiality in the article. Possible risks are noted throughout the article, such as potential attacks from hidden adversaries or deep leakage from gradient. Furthermore, both sides of the argument are presented equally throughout the article, providing an unbiased view on federated learning and its associated risks.