All notable changes to this project will be documented in this file.
We're super excited to announce FLUTE: Federated Learning Utilities for Testing and Experimentation, a platform for conducting high-performance federated learning simulations!
This first release fully focuses on implementing fast prototyping to validate different CL scenarios in an Federated environment.
- large scale simulation (millions of clients, sampling tens of thousands per round).
- multi-GPU and multi-node orchestration backed up by MPI.
- local or global differential privacy.
- model quantization.
- a variety of standard optimizers and aggregation methods.
- most model types including CNNs, RNNs, and Huggingface Transformers.
- extensibility, enabling new models, dataloaders, optimizers, and aggregators.
- local or cloud-based job staging using AzureML.
This release contain major changes in the communication backbone , in order
to run previous experiments you have already integrated in FLUTE, please make sure
to use torch.distributed instead of MPI to launch the jobs. For more documentation
about the new command, please refer to the README.
- 🏎 Better performance: Support for NCCL and Gloo as backend communication protocols.
- Improvements in GPU utilization and overall communication speed (on the order of minutes!) for projects with huge models and datasets.
- 🌟 Remove file type dependency on client.py, now FLUTE can receive any kind of dataset and even download the data on-the-fly. The data intantiation is completely under control of each task dataset.
- In older versions FLUTE only allowed
jsonandhdf5files, so the client could recognize it.
- In older versions FLUTE only allowed
- 🌟 Abstract classes for new models/dataloaders.
- 🌟 Allows Federated Learning with Personalization.
- Personalization allows you to leverage each client local data to obtain models that are better adjusted to their own data distribution. You can run the
cvtask in order to try out this feature.
- Personalization allows you to leverage each client local data to obtain models that are better adjusted to their own data distribution. You can run the
🔋 This release removes the restriction of the minimum number of GPUs available in FLUTE, allowing users to run experiments using a single-GPU worker by instantiating both: Server and clients on the same device. For more documentation about how to run an experiments using a single GPU, please refer to the README.
- 🌟 Include FedProx aggregation method