Top 10 Coding Tools For Federated Learning

Federated Learning was introduced to collaboratively learn a shared prediction model while keeping all the training data on the device. This enabled machine learning developers to build pipelines that wouldn't require to store the data in the cloud. The main drivers behind FL are privacy and confidentiality concerns, regulatory compliance requirements, as well as the practicality of moving data to one central learning location. Here are a few libraries (mostly by OpenMined) for developers that can help in building federated learning systems for the edge devices. The developers can write the model and training plan in normal PyTorch and PySyft, and syft.js