Distributed data classification is becoming more and more actual nowadays. The growing data and it’s decentralized nature forces to analyze data in distributed fashion. Many conventional algorithms tackle this problem well in case of centralized data, which differs from the case of distributed data. In distributed case may arise the problem of data privacy. We survey the distributed learning frameworks for classification and modifications of boosting algorithms.