Abstract: The federated learning method effectively solves the "data island" problem in the big data era and also ensures data privacy and security to a certain extent. However, many aspects of federated learning still face privacy risks. First, it summarizes the common privacy threats faced by federated learning, and summarizes corresponding privacy protection measures for different types of privacy threats. Secondly, it focuses on the discussion of differential privacy methods, and summarizes some implementation methods of differential privacy. Finally, based on differential privacy Privacy designs a privacy protection method suitable for federated learning systems.
Content directory:
1 Overview of Federated Learning
1.1 Definition of Federated Learning
1.2 Classification of federated learning
2 Privacy threats faced by federated learning
2.1 Privacy threats in federated learning
2.2 Model extraction attack
2.3 Model reverse attack
3 Measures to Defend against Privacy Threats
4 Overview of Differential Privacy Methods
4.1 Basic definition of differential privacy
4.2 Sensitivity analysis of differential privacy
4.3 Implementation methods of differential privacy
5 Use differential privacy to protect the security of federated learning systems
5.1 Federated Learning Server’s Protection of Privacy and Security
5.2 Privacy and security protection of federated learning participants
5.3 Experimental analysis
6 Conclusion
With the increasing development of big data technology, data security issues such as data leakage, illegal data access, and data tampering have attracted more and more attention from countries around the world. Increasingly strict management of the privacy and security of user data will be a world trend. Federated Learning (FL) is a distributed machine learning strategy that generates a global model by learning training data from multiple distributed edge clients, effectively solving the "data island" problem. With the development of federated learning technology, federated learning has achieved great improvements in performance on the basis of protecting user training data and making data "available and invisible", and is getting closer to the performance of data-centralized learning.
Training high-precision federated learning models requires large-scale, high-quality data support, and high