Aiming at the problems of the leakage of model parameters， that the untrusted server may return wrong aggregation results， and the users participating in training may upload wrong or low-quality model parameters in the process of federated learning in edge computing scenarios， a privacy-preserving federated learning algorithm based on blockchain in edge computing was proposed. In the training process， firstly， the global model parameters were trained on the local dataset of each user by the users， and the model parameters obtained by training were uploaded to neighboring edge nodes through secret sharing， thereby protecting the local model parameters of the users. Secondly， the Euclidean distances between the shares of model parameters received by the edge nodes were computed， and the results of these calculations were uploaded to the blockchain. Finally， the Euclidean distances between model parameters were reconstructed by the blockchain， and then the global model parameter was aggregated after removing the poisoned updates. The security analysis proves the security of the proposed algorithm： even in the case of collusion of a part of edge nodes， the users’ local model parameter information will not be leaked. At the same time， the experimental results show the high accuracy of this algorithm： the accuracy of the proposed algorithm is 94.2% when the proportion of poisoned samples is 30%， which is close to the accuracy of the Federated Averaging （FedAvg） algorithm without poisoned samples （97.8%）， and the accuracy of FedAvg algorithm is decreased to 68.7% when the proportion of poisoned samples is 30%.

In order to solve the problems that the iterative efficiencies of the existing privacy protection k-means clustering schemes are low， the server in the centralized differential privacy preserving k-means clustering scheme may be attacked， and the server in the localized differential privacy protection k-means clustering scheme may return wrong clustering results， a Multi-party Privacy Protection k-means Clustering Scheme based on Blockchain （M-PPkCS/B） was proposed. Taking advantages of localized differential privacy technology and the characteristics of the blockchain such as being open， transparent， and non-tamperable， firstly， a Multi-party k-means Clustering Center Initialization Algorithm （M-kCCIA） was designed to improve the iterative efficiency of clustering while protecting user privacy， and ensure the correctness of initial clustering centers jointly generated by the users. Then， a Blockchain-based Privacy Protection k-means Clustering Algorithm （Bc-PPkCA） was designed， and a smart contract of clustering center updating algorithm was constructed. The clustering center was updated iteratively by the above smart contract on the blockchain to ensure that each user was able to obtain the correct clustering results. Through experiments on the datasets HTRU2 and Abalone， the results show that while ensuring that each user obtains the correct clustering results， the accuracy can reach 97.53% and 96.19% respectively， the average iteration times of M-kCCIA is 5.68 times and 2.75 times less than that of the algorithm of randomly generating initial cluster center called Random Selection （RS）.