PaddleFL Release 1.2.0

Major Features

• Support CUDA for ABY3 protocol.

• Suport NCCL communication for CUDA mode.

• cuda_demo added.

• Several bugs fixed.

Known Issues

• CPUPlace and PrivC protocol are not avaliable in gpu version, use cpu version image/whl instead.

PaddleFL Release 1.1.2

• Adds a new two-party MPC learning protocol: PrivC
• Adds examples of YoutubeDNN with_movielens on ABY3, and Linear & Logistic Regression on PrivC
• Provides APIs of online data sharing and revealing
• Supports underlying communication using GRPC
• Fixes several bugs
• Document updated

PaddleFL Release 1.1.0

• Add more MPC operators: conv2d, max pooling, softmax_with_cross_entropy, embedding, batch_norm, adam_optimizer, XavierInitializer, etc.
• Support more MPC models: CNN, FM, etc.
• Add more data process API: mean normal, one hot encoding.
• Add more model evaluation API: KS statistic, Auc, F1-score.
• Add paddle-serving API in data parallel to support model service after training.
• More examples to follow: fm_with_criteo, lenet_with_mnist, model encryption/decryption, psi demo, deploy_serving_after_training.

PaddleFL Release 1.0.0

v1.0.0 released

• Refactor the code for future update and maintenance.
• Add Federated Learning with MPC, which supports horizontal, vertical and transfer Federated Learning.
• Add load & transfer program from normal model to PaddleFL, supporting more models and scenarios.
• Add document and instructions for all demos to make them easy to follow.
• Provide official docker image, pull and use PaddleFL easily.

paddleFL release 0.2.0

v0.2.0 released
Support Kubernetes easy deployment
Add api for LEAF dataset which is in federated settings, supporting benchmark experiments.
Add FL-scheduler, acting as a central controller during the training phase.
Add FL-Submitter to support cluster task submission
Add secure aggregation algorithm
Support more optimizers in PaddleFL such as Adam