Deep Learning Approaches for Blockchain Scalability Through Sharding Technology

Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation-based sharding techniques frequently ignore node performance features and don't take care of leader election security issues. Furthermore, complete sharding reorganization significantly lowers the blockchain's overall performance in terms of overhead and time. In this work, we present a reputation-based dynamic reorganization network sharding strategy that provides alternate leaders to preserve the stability of the sharding system while also integrating node performance and behavioral aspects into the reputation computation. Considering this, the nodes are judiciously distributed via the sharding's partial reorganization technique to balance the processing capability of each shard, hence enhancing the system's overall performance and liveliness. According to simulation data, this sharding strategy successfully lowers the likelihood of a consensus failure, resolves the collusion attack issue brought on by the gathering of hostile nodes within shards, and lowers the blockchain system's latency. This offers solid proof of the blockchain sharding system's dependability and efficiency.