flame

README

🔥 Quickstart! (Ubuntu) 🔥

🔥 Quickstart! (macOS) 🔥

Flame is a platform that enables developers to compose and deploy federated learning (FL) training workloads easily. The system is comprised of a service (control plane) and a python library (data plane). The service manages machine learning workloads, while the python library facilitates composition of ML workloads. And the library is also responsible for executing FL workloads. With extensibility of its library, Flame can support various experimentations and use cases.

Prerequisites

The target runtime environment is Linux. Development has been mainly conducted under macOS environment. One should first set up a development environment. For more details, refer to here.

This repo has the following directory structure:

flame
 ├── CODE_OF_CONDUCT.md
 ├── CONTRIBUTING.md
 ├── LICENSE
 ├── Makefile -> build/Makefile
 ├── README.md
 ├── api (specification of REST API for flame apiserver)
 ├── build (configuration files for building flame binaries and container image)
 ├── cmd (source files for flame control plane)
 ├── docs (document folder)
 ├── examples (example folder)
 ├── fiab (dev/test env in a single box)
 ├── go.mod
 ├── go.sum
 ├── lib (python library for core flame data plane)
 ├── lint.sh
 ├── pkg (go packages for cmd)
 └── scripts (utility scripts)

Supported Algorithms/Mechanisms

Method	Note
FedAvg	https://arxiv.org/pdf/1602.05629.pdf
FedYogi	https://arxiv.org/pdf/2003.00295.pdf
FedAdam	https://arxiv.org/pdf/2003.00295.pdf
FedAdaGrad	https://arxiv.org/pdf/2003.00295.pdf
FedProx	https://arxiv.org/pdf/1812.06127.pdf
FedBuff	Asynchronous FL (https://arxiv.org/pdf/2106.06639.pdf and https://arxiv.org/pdf/2111.04877.pdf); secure aggregation is excluded
FedDyn	https://arxiv.org/pdf/2111.04263.pdf
OORT	https://arxiv.org/pdf/2010.06081.pdf; client selection algorithm / mechanism; experimental release
Hierarchical FL	https://arxiv.org/pdf/1905.06641.pdf; a simplified version where k2 = 1; support both synchronous and asynchronous FL
Hybrid FL	A hybrid approach to combine federated learning with ring-reduce; topology motivated from https://openreview.net/pdf?id=H0oaWl6THa

Documentation

A full document can be found here. The document will be updated on a regular basis.

Support

We welcome feedback, questions, and issue reports.

• Maintainers' email address: flame-github-owners@cisco.com
• GitHub Issues

Citation

@inproceedings{flame2023,
    author = {Harshit Daga and Jaemin Shin and Dhruv Garg and Ada Gavrilovska and Myungjin Lee and Ramana Rao Kompella},
    title = {Flame: Simplifying Topology Extension in Federated Learning},
    year = {2023},
    booktitle = {Proceedings of the 2023 ACM Symposium on Cloud Computing},
    keywords = {Federated Learning, Distributed Machine Learning},
    series = {SoCC '23}
}