# alibaba/FederatedScope

An easy-to-use federated learning platform

Repository: https://github.com/alibaba/FederatedScope
Canonical: https://ross.abutalabs.com/products/federatedscope
Homepage: https://www.federatedscope.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: federated-learning, machine-learning, pytorch
Last push: 2024-08-10T04:52:35+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1623, "days_push": 753, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1540, forks 261 (observed 2026-08-28T04:05:00.527221+00:00)

## What it is
FederatedScope is a comprehensive federated learning platform built on PyTorch with an event-driven architecture. It provides convenient usage and flexible customization for various federated learning tasks in both academia and industry.

## Use cases
- run federated learning experiments across simulated clients
- benchmark federated learning algorithms
- research privacy-preserving machine learning
- simulate federated backdoor attacks and defenses
- tune hyperparameters for federated training
- train models on decentralized data without sharing raw data

## When to choose
- you need a flexible, customizable federated learning framework for research
- you want an event-driven architecture that is easy to extend with new FL algorithms
- you need benchmarks for FL tasks like HPO or backdoor attacks

## When to avoid
- you need production-grade cross-device federated learning on real edge devices
- you want a simple off-the-shelf FL solution without customization
- your project does not use PyTorch

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, simulation, benchmarking
- domain: machine-learning, artificial-intelligence, privacy, developer-tools
- platform: python, cross-platform
- tags: federated-learning, pytorch, event-driven-architecture, privacy-preserving, research

## Member repositories
- alibaba/FederatedScope (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:00.527221+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:50.073858+00:00, confidence not recorded.
  - readme: https://github.com/alibaba/FederatedScope (fetched 2026-08-28T04:05:00.527221+00:00, sha 7e3ad6867ce7)
- Data as of 2026-08-30T08:39:29.467469+00:00.