[[{“value”:”Federated Learning (FL) is an emerging ML training paradigm where clients own their data and collaborate to train a global model without revealing any data to the server and other participants.
Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However, existing open-source tools do not offer the efficiency required to simulate FL on larger and more realistic FL datasets. We introduce pfl-research, a fast, modular, and easy-to-use Python framework for simulating FL. It supports TensorFlow, PyTorch, and non-neural network models, and is tightly…”}]] [[{“value”:”Federated Learning (FL) is an emerging ML training paradigm where clients own their data and collaborate to train a global model without revealing any data to the server and other participants.
Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However, existing open-source tools do not offer the efficiency required to simulate FL on larger and more realistic FL datasets. We introduce pfl-research, a fast, modular, and easy-to-use Python framework for simulating FL. It supports TensorFlow, PyTorch, and non-neural network models, and is tightly…”}]] Read More