PaddleFL is an open source federated learning framework based on PaddlePaddle. Researchers can easily replicate and compare different federated learning algorithms with PaddleFL. Developers can also benefit from PaddleFL in that it is easy to deploy a federated learning system in large scale distributed clusters. In PaddleFL, several federated learning strategies will be provided with application in computer vision, natural language processing, recommendation and so on. Application of traditional machine learning training strategies such as Multi-task learning, Transfer Learning in Federated Learning settings will be provided. Based on PaddlePaddle's large scale distributed training and elastic scheduling of training job on Kubernetes, PaddleFL can be easily deployed based on full-stack open sourced software. Data is becoming more and more expensive nowadays, and sharing of raw data is very hard across organizations. Federated Learning aims to solve the problem of data isolation and secure sharing of data knowledge among organizations. The concept of federated learning is proposed by researchers in Google [1, 2, 3]. PaddleFL implements federated learning based on the PaddlePaddle framework. Application demonstrations in natural language processing, computer vision and recommendation will be provided in PaddleFL. PaddleFL supports the current two main federated learning strategies: vertical federated learning and horizontal federated learning. Multi-tasking learning  and transfer learning  in federated learning will be developed and supported in PaddleFL in the future.