Getting started with federated learning
- Federated Learning for image classification introduces the key parts of the Federated Learning (FL) API, and demonstrates how to use TFF to simulate federated learning on federated MNIST-like data.
Federated Learning for text generation further demonstrates how to use TFF's FL API to refine a serialized pre-trained model for a language modeling task.
Tuning recommended aggregations for learning shows how the basic FL computations in
tff.learningcan be combined with specialized aggregation routines offering robustness, differential privacy, compression, and more.
Getting started writing custom federated computations
- Building Your Own Federated Learning Algorithm shows how to use the TFF Core APIs to implement federated learning algorithms, using Federated Averaging as an example.
Simulation best practices
High-performance simulations with TFF describes how to setup and configure the high performance TFF runtime.
TFF simulation with accelerators (GPU) shows how TFF's high-performance runtime can be used with GPUs.
Intermediate and advanced tutorials
Random noise generation points out some subtlities with using randomness in decentralized computations, and proposes best practices and recommend patterns.
TFF for Federated Learning Research: Model and Update Compression demonstrates how custom aggregations building on the tensor_encoding API can be used in TFF.
Custom Federated Algorithms, Part 1: Introduction to the Federated Core and Part 2: Implementing Federated Averaging introduce the key concepts and interfaces offered by the Federated Core API (FC API).