Collective Intelligence,
Individual Privacy
Our AI gets smarter from millions of users without ever seeing your data. Federated learning brings the benefits of big data to privacy-respecting AI.
How Federated Learning Works
The model comes to your data, not the other way around.
Local Training
Your device trains the AI model using only your local data. Raw data never leaves your device.
Gradient Computation
The device computes what the model learned (gradients) without sharing the underlying data.
Differential Privacy
Noise is added to the gradients to mathematically guarantee no individual data can be extracted.
Secure Aggregation
Encrypted gradients are combined using cryptographic protocols. No single party sees raw updates.
Model Update
The global model is improved and sent back to all devices. Everyone benefits, no one is exposed.
Privacy Guarantees
Multiple layers of protection ensure your data stays private.
Zero Data Upload
Your raw data—browsing history, messages, files—never leaves your device. Period.
Differential Privacy
Mathematical guarantee (ε=1.0) that individual contributions cannot be reverse-engineered.
Secure Aggregation
Cryptographic protocols ensure no single entity can access individual model updates.
Verifiable Privacy
Our implementation is open-source and has been audited by independent researchers.
How We Use Federated Learning
Threat Detection
The model learns to identify new malware patterns from across millions of devices without any device revealing what threats it encountered.
Phishing Recognition
Learns to identify phishing emails and websites by aggregating patterns without seeing any individual's messages.
Network Optimization
Improves connection routing and server selection based on collective experience without tracking individual usage.
Privacy Analysis
Gets better at identifying tracking and fingerprinting techniques without logging your browsing history.
Federated vs Centralized Learning
| Aspect | Centralized | Federated |
|---|---|---|
| Data Location | Uploaded to servers | Stays on device |
| Privacy Risk | Data breaches possible | No data to breach |
| Regulatory Compliance | Complex | GDPR-friendly by design |
| Bandwidth Usage | High (raw data) | Low (only gradients) |
| Personalization | Server-side profiles | On-device adaptation |
Technical Implementation
| Framework | TensorFlow Federated + Custom Extensions |
| Privacy Budget | ε = 1.0 (Rényi Differential Privacy) |
| Aggregation Protocol | Secure Aggregation with Secret Sharing |
| Communication | Compressed gradients, 10-100x reduction |
| Model Architecture | Transformer-based, 7B parameters (quantized) |
| Training Rounds | Daily aggregation, hourly local updates |
Frequently Asked Questions
What if someone intercepts the model updates?+
Can the central server learn anything about me?+
How much bandwidth does federated learning use?+
Does this slow down my device?+
Privacy-First AI
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