Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Library
|
| gptkbp:aims_to |
protect user data
reduce bias in models |
| gptkbp:based_on |
gptkb:Graphics_Processing_Unit
|
| gptkbp:developed_by |
gptkb:Job_Search_Engine
|
| gptkbp:enables |
model training with privacy guarantees
|
| gptkbp:has |
gptkb:document
user community |
| https://www.w3.org/2000/01/rdf-schema#label |
Tensor Flow Privacy
|
| gptkbp:includes |
evaluation metrics
training algorithms |
| gptkbp:integrates_with |
gptkb:Tensor_Flow_Federated
|
| gptkbp:is |
flexible
open-source scalable widely used community-driven extensible used in research actively maintained compatible with various hardware used in industry used for collaborative learning part of AI ethics discussions part of AI safety research part of Tensor Flow ecosystem part of academic courses part of data science toolkits part of machine learning frameworks part of online learning platforms part of open-source projects part of privacy research initiatives part of workshops and conferences related to CCPA compliance related to GDPR compliance related to data privacy laws used for privacy-preserving machine learning used for secure data sharing used for sensitive data analysis |
| gptkbp:is_available_on |
gptkb:archive
|
| gptkbp:is_compatible_with |
gptkb:Graphics_Processing_Unit
|
| gptkbp:is_designed_for |
federated learning
|
| gptkbp:is_used_for |
natural language processing
time series analysis image classification differential privacy |
| gptkbp:offers |
differentially private SGD
|
| gptkbp:provides |
tutorials
API for privacy privacy-preserving techniques |
| gptkbp:released_in |
gptkb:2019
|
| gptkbp:supports |
gptkb:software_framework
gptkb:Tensor_Flow_2.x |
| gptkbp:utilizes |
noisy gradients
secure aggregation |
| gptkbp:written_in |
gptkb:Library
|
| gptkbp:bfsParent |
gptkb:Graphics_Processing_Unit
|
| gptkbp:bfsLayer |
3
|