Decentralized Federated Learning with Secure Aggregation and Differential Privacy

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. This paper proposes a novel decentralized federated learning framework that integrates secure aggregation and differential privacy to mitigate these risks. The system utilizes secure aggregation techniques, such as homomorphic encryption, to protect individual model updates during the aggregation process. Simultaneously, differential privacy mechanisms are employed to limit the amount of information leaked about individual clients' data. The decentralized nature of the framework enhances robustness and scalability. This approach significantly strengthens the privacy guarantees of FL while maintaining model accuracy and efficiency. The core claim of this work is that enhancing security and privacy is crucial for the wider adoption of federated learning. The proposed mechanism combines secure aggregation and differential privacy in a decentralized federated learning framework.