Byzantine Resistant Secure Blockchained Federated Learning at the Edge

The emerging blockchained federated learning, known for its security properties such as decentralization, immutability and traceability, is evolving into an important direction of next-generation AI. With the booming edge computing technologies, blockchained federated learning can take advantage of computing, communication and storage resources geo-distributed at the edge, so that blockchained federated learning can gather edge intelligence from more widely distributed devices more efficiently. However, untrustworthy devices at the edge also bring serious security threats, namely byzantine attacks. Existing solutions focus on selecting local models that are most likely to be honest, rather than detecting byzantine models and identifying attackers, because verifying each local model separately brings intolerable verification delay. In this paper, we propose a byzantine resistant secure blockchained federated learning framework named BytoChain. BytoChain improves the efficiency of model verification by introducing verifiers to execute heavy verification workflows in parallel, and detects byzantine attacks through a byzantine resistant consensus Proof-of-Accuracy (PoA). We analyze how BytoChain can mitigate five types of attacks, and demonstrate its effectiveness by simulations. Finally, we envision some open issues about security, including attacks on privacy, confidentiality, and backdoors.

Automated summary

Blockchained federated learning, with its security properties such as decentralization, immutability, and traceability, is evolving into a key direction for next-generation AI. BytoChain, a byzantine resistant secure blockchained federated learning framework, improves model verification efficiency and detects byzantine attacks through a PoA consensus. By mitigating various types of attacks, BytoChain demonstrates effectiveness and addresses open issues in security.