FedLab
Distrobuted Federated Machine Learning Experimentation Service
Competing with very large infrastructure isn't easy, but scale can also be achieve horizontally having users/service providers sharing/aggregating resources. FedLab provides a turnkey federated experimentation service that anyone can deploy on their own hardware, able to run collaborative machine learning experiments across distributed nodes without centralizing sensitive data. Each FedLab instance manages local datasets, executes training runs, and shares only model updates (not raw data) with collaborating instances through a lightweight federation protocol. The service exposes a web-based dashboard for experiment configuration, progress monitoring, and result visualization. The project delivers an open-source, production-ready FedLab service packaged for NixOS, accompanying documentation, validate by a pilot deployment across nodes in the LLM+ research community.
- The project's own website: https://llmplus.ai/
Run by University of Cambridge
This project was funded through the NGI Fediversity Fund, a fund established by NLnet with financial support from the European Commission's Next Generation Internet programme, as a pilot programme under the aegis of DG Communications Networks, Content and Technology. NGI Fediversity is part of the Horizon Europe research and innovation programme under grant agreement No. 101136078.
