Federated learning enables users to train models without transmitting local data to central servers, preserving data privacy and reducing communication costs. However, federated learning also faces various challenges, such as single point of failure issue, byzantine attack, and a lack of incentive mechanism. In this paper, a trustworthy and incentivized federated learning framework is proposed. Firstly, a blockchain network is used as a substitute for the central server in federated learning, addressing the vulnerability of servers to attacks and dishonesty. Secondly, a multi-key homomorphic encryption mechanism is introduced to safeguard the privacy of local models. Furthermore, to address the challenge of detecting malicious models within encrypted models, this paper proposes a verification mechanism based on the cosine similarity of group models. Finally, this paper evaluates the contributions of multiple participants using Shapley Value. This process effectively coordinates and distributes the interests of multiple participants, ultimately incenting more users to engage in federated learning. Experimental results demonstrate that the framework proposed in this paper can still converge and maintain good training accuracy even in the presence of attacks.