A Collaborative Sharding Consensus Mechanism for Blockchain-Based Federated Learning in IoT

Integrating federated learning (FL) with blockchain technology provides an effective solution for enhancing data security and privacy in decentralized Internet of Things (IoT) ecosystems. However, challenges arise in efficiently achieving consensus and balancing the load across multiple shards, primarily due to the resource constraints and heterogeneity of IoT devices. This article introduces SynergyMining, a novel collaborative sharding consensus mechanism designed specifically for blockchain-based FL in IoT. SynergyMining leverages reinforcement learning to dynamically optimize consensus group (CG) selection, ensuring balanced workloads across shards and efficient resource utilization. Additionally, we propose a freshness and quality-aware FL framework with asynchronous model aggregation called FedFQ that dynamically adjusts aggregation weights based on the recency and quality of local models. This approach mitigates client instability and improves the efficiency of the global model aggregation process. Experimental results demonstrate that SynergyMining outperforms leading algorithms, including Monoxide, hidden Markov model (HMM), Elastic and optimal resource scheduling policies (ORSP) across key performance metrics. Specifically, compared to these algorithms, SynergyMining improves system throughput by 9.97% to 72.16%, final model accuracy by 1.18% to 5.53%, and reduces load imbalance by 6.65% to 16.29%. These advancements, combined with the freshness-aware aggregation, make SynergyMining a robust and scalable solution for IoT-based FL applications, offering significant improvements in efficiency, scalability, and security.