In the background of big data, ensuring credible data sharing is the basic requirement of data federation. Using blockchain technology to replace the traditional client-server architecture can improve the security of federated learning (FL). However, the huge communication cost and storage consumption generated by model parameter validation and data persistence in existing works have become problems that need to be solved urgently in data federation. To tackle these problems, an efficient decentralized federated learning framework (EDFL) is proposed, which can reduce the system overhead and significantly improve the learning efficiency of FL. Firstly, a consensus mechanism based on proof-of-contribution (PoC) is proposed where the election of the block generation is based on historical contribution instead of using the competition mechanism, thus, it can avoid the latency of the block generation caused by the mining process, and asynchronously alleviate the congestion in the model parameter validation. Secondly, a role-adaptive incentive algorithm is presented. Because the proposed algorithm is based on the work intensity of participating nodes and the role assigned by EDFL, it can motivate legitimate nodes to actively conduct model training and effectively identify malicious nodes. Thirdly, blockchain partition storage strategy is proposed. The proposed strategy enables multiple local reconstruction code chunks to be evenly distributed to nodes in the network, which reduces the local storage consumption and achieves higher efficiency of data recovery. Lastly, the learning efficiency, storage scalability, and security of EDFL are evaluated in real FEMNIST dataset. Experimental results show that EDFL outperforms the state-of-the-art blockchain-based FL framework from the above three aspects.