A Decentralized Sharding BFT Consensus Approach, for Efficient Decentralized DNN Inference Classification

The security and trustworthiness of participating DNN nodes are often overlooked during the design of modern Decentralized Deep Neural Networks (D-DNN). This paper introduces a shard-based distributed consensus protocol specifically tailored for DNN nodes operating over unreliable communication links. The proposed approach enhances D-DNN scalability, by enabling D-DNN systems having a large number of DNN nodes. This is achieved through a hierarchical consensus mechanism that partitions the D-DNN network into sub-networks (shards), leveraging Out-of-Distribution (OOD) detectors to localize and isolate the consensus process within each shard. Rather than randomly allocating DNN nodes into shards, the OOD detector can be employed to identify and group nodes with similar domain knowledge. This approach improves the overall D-DNN system robustness, by identifying and isolating malicious DNN nodes or once that have poor performance for a specific DNN task. Experimental results demonstrate improvements in the D-DNN system’s classification accuracy and reliability.