Decentralized Federated Learning with Secure Multi-Party Computation

Abstract

This paper proposes a novel approach to decentralized federated learning (DFL) that leverages secure multi-party computation (SMPC) to guarantee data privacy during collaborative model training. Traditional federated learning methods, while promoting data sharing for model improvement, inherently expose individual datasets to the central server, raising significant privacy concerns. Our framework addresses this limitation by employing SMPC protocols, enabling model updates to be aggregated securely without revealing the underlying data. This approach provides a strong privacy guarantee, combining the benefits of federated learning with robust privacy protection. We present a detailed description of the system architecture, the SMPC protocols utilized, and the mathematical formulation underpinning the aggregation process. The core claim of this work is the ability to enable collaborative model training across multiple parties without revealing individual data. The core mechanism relies on implementing a federated learning framework based on secure multi-party computation protocols, where model updates are aggregated securely without exposing the underlying data. This research represents a significant advancement in the field of privacy-preserving machine learning.