AI-Assisted Sharding Techniques for Blockchain Scalability
DOI:
https://doi.org/10.63345/Keywords:
Blockchain Scalability, Sharding, Reinforcement Learning, Graph Neural Networks, Adaptive Committees, Cross-Shard Transactions, Workload Prediction, Consensus, Data Availability, Zero-Knowledge ProofsAbstract
Sharding—partitioning a blockchain’s state and workload across multiple committees—remains one of the most promising approaches to scale permissionless ledgers without sacrificing decentralization. However, static shard layouts and coarse, rule-based reconfiguration often underperform when faced with bursty demand, non-stationary access patterns, or cross-shard dependencies. This manuscript proposes an AI-assisted sharding framework that integrates (i) graph-neural forecasters for short-horizon workload prediction on account/contract interaction graphs, (ii) a reinforcement-learning (RL) policy for online decisions about shard sizing, committee rotation, and cross-shard routing, and (iii) safety wrappers that enforce cryptographic and protocol constraints (e.g., minimum committee sizes, randomness beacons, and rotation limits) to preserve security.
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