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federated learning
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Explore more → **federated learning**
What is federated learning?
Federated learning (FL) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. This allows for privacy preservation and reduces the need for centralized data storage. It's commonly used in scenarios where data is distributed and sensitive, such as in mobile devices, healthcare, and finance.
What other technologies are related to federated learning?
federated learning Complementary Technologies
differential privacy
Differential privacy is a privacy-preserving technique often used in conjunction with federated learning to protect the privacy of individual data points.
homomorphic encryption
Homomorphic encryption allows computations to be performed on encrypted data, which is highly complementary to federated learning for privacy-preserving model training.
transfer learning
Transfer learning can be used to initialize or fine-tune models in federated learning, improving convergence speed and performance, especially when data is limited on individual devices.
Number of organizations that mention technology
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Which job functions commonly mention federated learning?
AI Engineer
0.1% of all AI Engineer jobs mention federated learning
View 120
jobs on Sumble
Machine Learning
0.1% of all Machine Learning jobs mention federated learning
View 103
jobs on Sumble
Data Scientist
0.1% of all Data Scientist jobs mention federated learning
View 183
jobs on Sumble
Researcher
171 Researcher jobs mention federated learning
View 171
jobs on Sumble
Data, Analytics & Machine Learning
435 Data, Analytics & Machine Learning jobs mention federated learning
View 435
jobs on Sumble
Which organizations are mentioning federated learning?
Oak Ridge National Laboratory
Public Administration
Université du Luxembourg
Educational Services
Microsoft
Scientific and Technical Services
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