FedShard: A Sharding-Based Federated Learning Framework With Layered Incentivization for IoT

The blockchain-based federated learning framework has garnered widespread attention to ensure data privacy and trustworthy computing in IoT devices. Improving the accuracy and scalability are paramount for the increasing demands of IoT tasks. However, existing solutions, such as SIFL and ChainFL, utilize traditional single-chain architecture and fixed models, which exhibit limitations in terms of model generalizability and scalability. To overcome the above problems, this paper proposesFedShard, an FL framework that integrates a layered dual-track incentive mechanism and a privacy knowledge distillation module.When implementingFedShard, we address two technical challenges: (1) to ensure efficient training among the numerous clients in the sharding architecture, we propose a layered dual-track incentive mechanism that provides both long- and short-term rewards; and (2) to enhance the framework’s convergence and privacy in sharding-based FL, we design a knowledge distillation module that incorporates local differential privacy. Furthermore, we propose a bucket-based hash ring to manage clients, enabling the framework to adapt to dynamic network environments. To validate the framework’s generalizability and scalability, we implementFedShardon a 48-core high-performance server using Fabric to conduct both on-chain and off-chain experiments. Our comprehensive experiments, comparingFedShardwith SIFL, PEFL, and ChainFL, reveal that our solution outperforms the state-of-the-art methods by achieving a notable 29% increase in accuracy and a 22% improvement in throughput.