DCMF-BFL: A decentralized consensus multi-factor approach for blockchain-based federated learning

Federated learning enables collaborative model training without sharing raw data, but many practical workflows still rely on a central coordinator for client selection, update aggregation, and reward accounting. This dependence introduces trust concentration, single-point failure risks, and limited transparency in contribution assessment. This paper presents DCMF-BFL, a blockchain-assisted decentralized federated learning framework that integrates committee-based aggregation, smart-contract coordination, IPFS-backed model exchange, and contribution-aware reputation evaluation. The proposed multi-factor score is not treated as a standalone scoring novelty; instead, it is used as an interpretable reputation interface that connects accuracy improvement, gradient quality, participation recency, and verified committee service with aggregation weighting, reward or penalty accounting, and committee governance. Experiments on image, text, and tabular classification tasks under IID and non-IID partitions show that DCMF-BFL consistently improves decentralized FL performance compared with FedAvg, FedProx, and q-FFL, achieving the highest final accuracy in 14 out of 16 dataset–partition settings. Further ablation, fairness, robustness, and overhead analyses demonstrate that the integrated reputation and committee-governance design improves client-level fairness, mitigates the influence of poisoned updates, and provides auditable decentralized coordination with measurable but manageable system overhead. Overall, the results show that interpretable contribution-aware reputation can be effectively coupled with blockchain-based committee governance to support more robust, fair, and transparent decentralized federated learning.