What is Federated Learning?
Federated learning enables distributed model training without sharing raw data. This approach protects privacy by training models across decentralized edge devices or servers, exchanging only model updates.
Technical Definition
A machine learning paradigm that keeps training data localized and only communicates parameter updates across devices.
Core Advantages
Data Privacy
Keeps sensitive data local while enabling collaboration between organizations.
Edge Computing Ready
Enables training machine learning models directly on smartphones, IoT devices, and embedded systems.
Collaborative Innovation
Builds better models through shared learning without compromising data confidentiality.
How Federated Learning Works
Step 1
Local models are trained separately on each client's data without sharing raw data.
Step 2
Model parameters or gradients from clients are aggregated at a central server.
Step 3
A global model is updated at the server to improve accuracy across all participants.
Secure Aggregation
Advanced cryptographic techniques ensure no individual client's contribution can be reversed-engineered.
Industry Applications
Healthcare
Enables collaborative medical research across institutions while protecting patient privacy.
Finance
Allows banks to build fraud detection systems without sharing customer transaction data.
IoT Devices
Powers mobile and edge AI solutions while maintaining user data anonymity.
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