A Survey of Federated Learning Privacy Preservation Techniques for Malicious Behavior Detection

Abstract

Centralized machine learning requires the centralization of data in one server for model training, the data of individuals must be transmitted to the centralized server using its raw form which resulting in serious privacy and security concerns. Federated learning is a decentralization machine learning technique which improves the issues of security and privacy related to traditional machine learning by enabling local model training on devices without sharing raw data with the centralized server. Federated learning includes multiple clients and one central server. Clients perform training on its own data while the server coordinates the overall federated learning process. In federated learning, raw data never leaves its own place, ensuring data confidentiality. Only local model updates, form each client are transmitted to the central server that organizes the learning process. The server performs aggregation on received local model updates. Following the aggregation process, the global model is then updated by the server. The final global model is used then for evaluation. However federated learning improves privacy along with security of centralized machine learning, it is still targeted by attacks through model updates transmitted between clients and server. To improve privacy along with security related to federated learning, privacy preservation techniques are integrated with federated learning. We propose a survey of privacy preservation techniques combined with federated learning to improve privacy and security and achieve a good balance between utility and privacy. Private Aggregation of Teacher Ensembles, Homomorphic Encryption, as well as Secure Multi-Party Computation represent the most popular used privacy preservation techniques with federated learning for malicious behavior detection.