SCAMA: A Smart-Contract-Driven Asynchronous Model Aggregation Framework for Decentralized Federated Learning

Mingzhi Mao, Xiangyuan Zhu, Jianguo Chen, Longxin Zhang, Guocheng Liao, Zulong Diao

Abstract: Due to the absence of a trusted model parameter sharing mechanism, model tampering, malicious uploads, and data inconsistency pose significant risks within the Federated Learning (FL) architecture. To address these challenges, this paper proposes an automated Smart Contract-based Asynchronous Model Aggregation (SCAMA) method to enhance model security in decentralized FL frameworks. We design a trigger-based asynchronous model submission mechanism driven by smart contracts. These contracts incorporate a dynamic adjustment strategy that can initiate global aggregation in real time. Furthermore, a decentralized parameter validity verification rule is embedded within the smart contract to ensure that submitted model updates align with the global optimization objective, thereby mitigating risks such as malicious uploads and training drift. Building upon this, we introduce a trustless model parameter-sharing strategy based on on-chain storage. The global model update process is encoded into the smart contract and executed through multiparty verifiable storage on a distributed ledger. During model aggregation, the smart contract utilizes encrypted hashing to verify the consistency of submitted models, ensuring that the parameter versions received by each node are traceable, reliable, and tamper-proof. Experimental results demonstrate that our method offers significant improvements in computational efficiency and model convergence speed compared with traditional synchronous FL approaches and existing asynchronous strategies.