FedOps: Federated Learning Lifecycle Operations Management Platform
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FedOps (fedops) is a platform that helps organizations effectively manage and coordinate their federated learning operations:
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FLScalize: It simplifies the application of data and models in a FL environment by leveraging Flower's Client and Server.
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Manager: The manager oversees and manages the real-time FL progress of both clients and server
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CE/CS: Contribution Evaluation and Client Selection processes based on their performance.
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CI/CD/CFL: the CI/CD/CFL system seamlessly integrates with a Code Repo, enabling code deployment to multiple clients and servers for continuous or periodic federated learning.
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Monitoring: The FL dashboard is available for monitoring and observing the lifecycle of FL clients and server
FedOps Tutorial
FedOps has developed a web service to manage the lifecycle operations of federated learning on real devices.
- Install FedOps Library
$ pip install fedops
Run FedOps Agent Studio
Docker Desktop 또는 Docker Engine이 설치된 환경에서 다음 명령으로 실행합니다.
fedops run agent-studio
이 명령은 현재 OS와 CPU architecture에 맞는
gachonccl/fedops-agent-studio:latest 이미지를 확인하고,
~/fedops-workspace를 로컬 Workspace로 연결한 뒤 브라우저를 엽니다.
Task별 Python 환경과 데이터는 Workspace에 유지하고, 반복 환경 Sync에 사용하는 uv
package/Python cache는 fedops-agent-studio-uv Docker volume에 보존합니다. 따라서
컨테이너와 이미지가 교체되어도 cache를 재사용하며, 특히 Windows Docker Desktop에서
불필요한 재다운로드와 압축 해제 비용을 줄입니다.
NVIDIA Container Runtime이 준비된 Linux/Windows 환경에서는 GPU를 자동 사용하고,
그 외 환경에서는 CPU 모드로 실행합니다.
기본 Docker bind address는 0.0.0.0입니다. 따라서 같은 실행으로
localhost, 127.0.0.1, 호스트의 LAN IP에서 접속할 수 있습니다. 호스트
네트워크에 공개되므로 OS firewall에서 허용 범위를 관리해야 합니다. 로컬 장치에서만
접속하려면 다음 옵션을 사용합니다.
fedops run agent-studio --bind-address 127.0.0.1
실제 컨테이너를 변경하지 않고 실행 구성을 먼저 확인할 수 있습니다.
fedops run agent-studio --dry-run
Workspace, Studio port 또는 GPU 모드를 명시하려면 다음 옵션을 사용합니다.
fedops run agent-studio --workspace ~/fedops-workspace --port 24368 --gpu auto
Agent Studio와 로컬 Host integration을 종료하고 결과를 확인하려면 다음 명령을 사용합니다.
fedops stop agent-studio
Real Devices
Single Machine
Community
Paper
FLScalize: Federated Learning Lifecycle Management
@article{Cognitive Computing Lab,
title={FLScalize: Federated Learning Lifecycle Management},
author={Semo Yang; Jihwan Moon; Jinsoo Kim; Kwangkee Lee; Kangyoon Lee},
journal={IEEE Access},
Page(s)={47212 - 47222}
DOI={10.1109/ACCESS.2023.3275439}
year={2023}
}
Support
For any questions or issues, please contact the FedOps support team at gyom1204@gachon.ac.kr
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