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Federated learning (FL) is an emerging distributed machine learning paradigm without revealing private local data for privacy-preserving. However, there are still limitations. On one hand, user' privacy can be deduced from local outputs. On the other hand, privacy, efficiency, and accuracy are hard to fulfill for conflicting goals. To tackle these problems, we propose a …
Train machine learning models on sensitive user data has raised increasing privacy concerns in many areas. Federated learning is a popular approach for privacy protection that collects the local gradient information instead of real data. One way to achieve a strict privacy guarantee is to apply local differential privacy into federated learning. However, previous …