FedTensor
Train AI models across distributed data without sharing sensitive information. Privacy-preserving federated learning that scales to 1000+ clients.
10+ aggregation algorithms, real-time monitoring, PyTorch/TensorFlow/Scikit-learn support. HIPAA-ready. Docker & Kubernetes deployment. Simple 3-line client SDK.
1000+
Concurrent Clients
10+
Algorithms
3 lines
To Get Started
How Federated Learning Works
Your data stays where it is. Only model updates travel.
1. Data Stays Local
Each client (hospital, bank, device) keeps its data on-site. No raw data ever leaves.
2. Train Locally
Clients train the model on their own data, producing only weight updates — not the data itself.
3. Aggregate Securely
The server combines updates using FedAvg, FedProx, or SCAFFOLD — without ever seeing the data.
4. Global Model Improves
After multiple rounds, the global model learns from everyone's data — without anyone sharing it.
Platform Features
Everything you need for production federated learning.
Privacy by Design
Differential privacy, secure aggregation, and encrypted communication. HIPAA and GDPR compliant.
Hierarchical Aggregation
Orchestrator → Coordinators → Clients architecture for massive scale across data centers and edge devices.
Real-Time Monitoring
Live dashboard with loss curves, accuracy metrics, client status, and training progress per round.
Framework Agnostic
PyTorch, TensorFlow, Scikit-learn — use your preferred ML framework with zero lock-in.
10+ Algorithms
FedAvg, FedProx, SCAFFOLD, FedNova, FedBN, and more — choose the right strategy for your use case.
Docker & Kubernetes
One-command deployment with Docker Compose or Helm charts. Production-ready from day one.
Developer Experience
3 Lines to Federate Your Model
No complex APIs. No infrastructure headaches. Import FedTensor, wrap your model, and start training across distributed clients in minutes.
- Works with any PyTorch, TF, or Scikit-learn model
- Automatic serialization and communication
- Built-in fault tolerance and client reconnection
from fedtensor import FederatedClient
# Wrap your existing model — that's it
client = FederatedClient(model=my_model, data=my_data)
client.train(rounds=50, server="fedtensor.example.com")Use Cases
Where federated learning makes the biggest impact.
Healthcare
Train diagnostic models across hospitals without sharing patient records. HIPAA-compliant by architecture.
Finance
Fraud detection across banks without pooling transaction data. Each institution keeps full control.
Edge / IoT
Train on-device models (phones, sensors, vehicles) without uploading data to the cloud.
Manufacturing
Predictive maintenance across factories. Each plant trains locally, all benefit from the shared model.
NLP & Generative AI
Fine-tune language models on private corpora across organizations — legal, medical, government.
Research & Academia
Multi-institution collaborations without data sharing agreements. Publish results, not datasets.
Platform Screenshots
See FedTensor in action — from experiment setup to training results.
Dashboard — monitor experiments, clients, and training progress
Experiment management with real-time metrics
Client management and status monitoring
Training progress with loss and accuracy charts
Hierarchical architecture — orchestrator, coordinators, clients
Experiment configuration and algorithm selection
Aggregation algorithms
Docker & K8s deployment
Simple 3-line client SDK
Ready to Train AI Without Sharing Data?
Whether you're a researcher, a hospital, or a Fortune 500 — FedTensor adapts to your scale and compliance requirements.