Federated Learning
by Community · free · Last verified 2026-03-17
A machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging the data itself. It enables collaborative model training by aggregating locally computed updates, thereby preserving data privacy, security, and sovereignty.
https://flower.dev/ ↗B
B—Above Average
Adoption: B+Quality: AFreshness: ACitations: AEngagement: F
Specifications
- License
- Apache-2.0
- Pricing
- free
- Capabilities
- Federated Averaging (FedAvg), Secure Aggregation using MPC or homomorphic encryption, Differential Privacy integration for formal privacy guarantees, Cross-Silo Federation for organization-level collaboration, Cross-Device Federation for training on user devices, Communication-efficient algorithms (e.g., quantization, sparsification), Personalized Federated Learning for user-specific models, Asynchronous participation and update aggregation, Support for non-IID (Not Independent and Identically Distributed) data
- Integrations
- [object Object], [object Object], [object Object], [object Object]
- Use Cases
- [object Object], [object Object], [object Object], [object Object], [object Object]
- API Available
- No
- Difficulty
- advanced
- Prerequisites
- distributed-systems, machine-learning, differential-privacy
- Supported Agents
- Tags
- federated-learning, privacy-preserving-ml, distributed-training, on-device-ml, decentralized-ai, edge-ai, collaborative-ml, data-sovereignty, secure-aggregation, differential-privacy, cross-silo, cross-device
- Added
- 2026-03-17
- Completeness
- 0.95%
Index Score
67.5Adoption
75
Quality
85
Freshness
87
Citations
82
Engagement
0