Propagable Backdoors over Blockchain-based Federated Learning via Sample-Specific Eclipse

Blockchain-based federated learning, also being named as swarm learning, is perceived to have great potential to support decentralized and privacy-enhancing big data processing. However, numerous serious vulnerabilities found on blockchain and federated learning enforce us to concern about the security of swarm learning. Some seemingly-unrelated combinations of known vulnerabilities may derive highly-converted and unknown threats to swarm learning. In this paper, we first investigate the security threats of the swarm learning framework. And then, leveraging backdoor attacks and eclipse attacks, a novel hybrid vulnerability that can furtively propagate backdoors among swarm learning nodes is identified. To speed up the backdoor propagation and reduce attack costs, a sample-specific eclipse (SSE) strategy that can select the swarm network node with a high data contribution rate as the attack object is also proposed. Finally, by adjusting the trigger size, the data distribution rate, and the poisoning ratio, we conduct various comparison experiments to validate the feasibility of the proposed methods. To the best of our knowledge, this is the first article to study the epidemicity of backdoors in swarm learning.