DRM-FL: A Decentralized and Randomized Mechanism for Privacy Protection in Cross-Silo Federated Learning Approach

DRM-FL: Cross-Silo Federated Learning 접근법의 프라이버시 보호를 위한 분산형 랜덤화 메커니즘

Recently, federated learning (FL) has increased prominence as a viable approach for enhancing user privacy and data security by allowing collaborative multi-party model learning without exchanging sensitive data. Despite this, most present FL systems still depend on a centralized aggregator to generate a global model by gathering all submitted models from users, which could expose user privacy and the risk of various threats from malicious users. To solve these issues, we suggested a safe FL framework that employs differential privacy to counter membership inference attacks during the collaborative FL model training process and empowers blockchain to replace the centralized aggregator server.