BSFL: A blockchain-oriented secure federated learning scheme for 5G

Abstract

Ensuring data security, privacy, and defense against poisoning attacks in 5G intelligent scheduling has become a critical research priority. To address this, this paper proposes BSFL, a verifiable and secure federated learning scheme resistant to poisoning attacks, integrating blockchain technology. This scheme fully leverages the high speed and low latency characteristics of 5G networks, enabling rapid scheduling and real-time processing of smart devices, thus providing robust data support for federated learning. By incorporating the decentralized, immutable, and transparent nature of blockchain, we design a blockchain-based federated learning framework that facilitates verification of feature results and comparison of data features among participants, ensuring the security and reliability of scheduling data. Moreover, it prevents denial-of-service attacks to a certain extent. Experimental results demonstrate that this scheme not only significantly improves the efficiency and accuracy of federated learning but also effectively mitigates the potential threat of poisoning attacks, providing a robust security guarantee for federated learning in 5G intelligent scheduling environments.