Changelog

appfl v1.4.0

New Features

  • Add Ray into communicator, with documentation available here. (#271)

  • Make mpi4py optional during installation. (#264)

Deprecated

  • Derecate the usage of comm_configs.globus_compute_configs for AWS S3 configurations, which is replaced by comm_configs.s3_configs.

appfl v1.3.0

New Features

  • Integrate APPFL with MONAI to use MONAI bundles for federated learning, with documentation available here. (#251)

  • Add support for Multi-GPU training using PyTorch DDP, with documentation available here. (#254)

  • Integrate ProxyStore into Globus Compute and gRPC communication protocols for data transmission, with documentation available here. (#252, #259)

  • Add three colab-based tutorials at here for running APPFL on Google Colab (#255).

appfl v1.2.1

New Features

  • Remove redundant experiment configurations. (#228)

  • Enhance safety for Globus Compute by only sending a trigger function. (#227)

appfl v1.2.0

New Features

  • Improve client name display for running FL experiments by specifying client_id in the client configuration file.

  • Add documentation for using APPFL on ALCF Polaris at here.

  • Allow users to send payload of arbitrary size for custom actions in gRPC communication.

  • Add more tests for FL experiments under different scenarios: serial, MPI, batched MPI, and gRPC.

  • Integrate wandb for logging training metadata such as training and validation losses into client trainer, with documentation available here.

Bug Fixes

  • Fix path issues when running APPFL on Windows.

  • Fix batched MPI issue with compression.

  • Fix some other small bugs and bump the version of few dependencies.

appfl v1.1.0

New Features

  • Support batched MPI, with documentation available here.

  • Add more data readiness metrics such as PCA plot in this pull request.

  • Backend support for service.appfl.ai.

  • Add documentation for service.appfl.ai at here.

  • Add logging capabilities to the server side to log the training metadata such as the training and validation losses.

  • Change documentation theme to furo.

appfl v1.0.5

New Features

  • Add the feature to generate data readiness reports on all client data.

  • Update the documentation for adding custom action at here.

appfl v1.0.4

New Features

  • Add documentation for using APPFL with Globus Compute for secure distributed training at here.

Bug Fixes

  • Fix an issue with Globus Compute at this commit.

appfl v1.0.3

New Features

Bug Fixes

  • Handle corner cases for server aggregators when the keys in client local models are not consistent with the global model keys.

appfl v1.0.2

New Features

  • Add a new command line interface (CLI), appfl-setup-ssl to create necessary certificates for creating SSL-secured gRPC connections between the server and clients.

  • Add a tutorial on how to use the CLI, appfl-setup-ssl, to create certificates for the server and clients, and enable SSL-secured gRPC connections between the server and clients.

  • Add a detailed step-by-step tutorial on how to define custom action with an example to generate a data readiness report on all client data at here.

  • Add a APPFL YouTube channel to provide video tutorials on how to use APPFL for federated learning research in the future.

Bug Fixes

  • Fix the issue regarding client gradient clipping. The clipping is now applied before weights update.

appfl v1.0.1

New Features

  • For the aggregators, the model architecture is set to be an optional initialization parameter, and the aggregators only aggregate the parameters sent by the clients instead of the whole set of model parameters. This is useful when doing federated fine-tuning or federated transfer learning where only part of model parameters are updated / the model architecture is unknown to the aggregator.

  • Support easy integration of custom trainer/aggregator: user only needs to provide the custom trainer/aggregator class name and the path to the definition file in the configuration file to use it, instead of modifying the source code.

  • Add a detailed step-by-step tutorial on how to use APPFL to fine-tune a ViT model with a custom trainer.

appfl v1.0.0

Version 1.0.0 of appfl is a major release that refactors the entire codebase to make it more modular, extensible, and functional, while remains backward compatibility with the previous version. The release also included the following changes:

New Features

  • Define server and client agents to act on behalf of the FL server and clients to conduct FL experiments.

  • Simplify the configuration process for launching FL experiments by only providing a single YAML file for the server and a YAML file for each client.

  • Rebuild the communicator module, supporting MPI, gRPC, and Globus Compute, to robustly exchange model parameters as well as task metadata between the server and clients in both synchronous and asynchronous FL experiment settings.

  • Implement Globus-based authentication for secure distributed training with gRPC and Globus Compute - only members within the same specific Globus group can participate in the FL experiment.

  • Integrate several lossy and error-bounded lossless compressors to the communicator module for efficient model compression.

  • Add documentation for the new version available at appfl.ai

Deprecated

  • The previous version of appfl is still seamlessly supported but deprecated and no longer maintained. Users are encouraged to upgrade to the new version for better performance, functionality, and extensibility.

  • Examples and tutorials for the previous version are still available in the examples/examples_legacy directory of the Github appfl repository.