Appfl

New Features

- Add [Ray](https://www.ray.io/) into communicator, with documentation available [here](https://appfl.ai/en/latest/tutorials/examples_ray.html). (#271)
- Make mpi4py optional during installation. (264)

Deprecated

- Deprecate the usage of `comm_configs.globus_compute_configs` for AWS S3 configurations, which is replaced by `comm_configs.s3_configs`.

New Features

- Make MPI installation optional

New Features

- Integrate APPFL with MONAI to use MONAI bundles for federated learning, with documentation available [here](https://appfl.ai/en/latest/tutorials/examples_monai.html). #251
- Add support for Multi-GPU training using PyTorch DDP, with documentation available [here](https://appfl.ai/en/latest/tutorials/examples_gpuclusterrun.html#multi-gpu-training). 254
- Integrate [ProxyStore](https://docs.proxystore.dev/latest/) into Globus Compute and gRPC communication protocols for data transmission, with documentation available [here](https://appfl.ai/en/latest/tutorials/examples_globus_compute.html#extra-integration-with-proxystore). 252, 259
- Add three colab-based tutorials at [here](https://appfl.ai/en/latest/notebooks/index.html#colab-notebooks) for running APPFL on Google Colab 255

**Full Changelog**: https://github.com/APPFL/APPFL/compare/v1.2.1...v1.3.0

New Features

- Enhance safety for Globus Compute by only sending a trigger function. 227
- Remove redundant experiment configurations. 228

**Full Changelog**: https://github.com/APPFL/APPFL/compare/v1.2.0...v1.2.1

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](https://appfl.ai/en/latest/tutorials/examples_gpuclusterrun.html#grpc-simulation-on-polaris-cluster).
- 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](https://appfl.ai/en/latest/tutorials/examples_wandb.html).

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.

New Features

- Support batched MPI, with documentation available [here](https://appfl.ai/en/latest/tutorials/examples_batched_mpi.html).
- Add more data readiness metrics such as PCA plot in PR 208
- Backend support for [service.appfl.ai](https://appflx.link/).
- Add documentation for service.appfl.ai at [here](https://appfl.ai/en/latest/tutorials/appflx/index.html).
- Add logging capabilities to the server side to log the training metadata such as the training and validation losses.
- Change documentation theme to `furo`.

Community Standards
- Add [pull request template](https://github.com/APPFL/APPFL/blob/main/.github/pull_request_template.md) and issue templates
- Add [contribution guidance](https://appfl.ai/en/latest/contribution/index.html)
- Add dependabot for auto github action version check