Federated Learning
by Community · free · Last verified 2026-03-17
Trains machine learning models across decentralized data sources (devices or organizations) without centralizing raw data, using local computation and aggregated gradient updates. Enables collaborative model improvement while preserving data sovereignty and regulatory compliance.
https://flower.dev/ ↗B
B—Above Average
Adoption: B+Quality: AFreshness: ACitations: AEngagement: F
Specifications
- License
- Apache-2.0
- Pricing
- free
- Capabilities
- FedAvg-aggregation, secure-aggregation, cross-silo-federation, cross-device-federation, communication-efficient-training
- Integrations
- Flower, PySyft, TensorFlow Federated, OpenFL
- Use Cases
- Healthcare model training across hospital networks, On-device keyboard prediction without data upload, Cross-enterprise fraud detection
- API Available
- No
- Difficulty
- advanced
- Prerequisites
- distributed-systems, machine-learning, differential-privacy
- Supported Agents
- Tags
- federated-learning, privacy, distributed-training, on-device-ml
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
67.5Adoption
75
Quality
85
Freshness
87
Citations
82
Engagement
0