PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks
This addresses security issues for distributed learning systems in 5G networks, but it appears incremental as it adapts existing blockchain techniques to a specific domain.
The paper tackles the challenge of model safety in distributed machine learning within 5G networks, which are vulnerable to byzantine attacks, by proposing PIRATE, a secure framework based on blockchain sharding, though no concrete performance numbers are provided.
In the fifth-generation (5G) networks and the beyond, communication latency and network bandwidth will be no more bottleneck to mobile users. Thus, almost every mobile device can participate in the distributed learning. That is, the availability issue of distributed learning can be eliminated. However, the model safety will become a challenge. This is because the distributed learning system is prone to suffering from byzantine attacks during the stages of updating model parameters and aggregating gradients amongst multiple learning participants. Therefore, to provide the byzantine-resilience for distributed learning in 5G era, this article proposes a secure computing framework based on the sharding-technique of blockchain, namely PIRATE. A case-study shows how the proposed PIRATE contributes to the distributed learning. Finally, we also envision some open issues and challenges based on the proposed byzantine-resilient learning framework.