# Architecture

SoraChain AI is designed as a privacy-first, decentralized AI training infrastructure. Its architecture integrates **Federated Learning** with **Blockchain**, enabling secure, verifiable, and collaborative model development across edge devices. The architecture is built around the **Collaborative Model Update (CMU)** framework and operates through three primary layers inside interoperable subnets.

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### **1. Global Workflow Layer (Left Section of Diagram)**

This layer showcases the **complete lifecycle** of model training and validation in a decentralized setting.

* **Local Training on Edge Devices**\
  Devices such as smartphones, wearables, and IoT systems train models locally on private data.
* **Global Model on IPFS**\
  The Global model is uploaded to [IPFS](https://ipfs.tech/) for decentralized, redundant storage. Devices joining in between can directly pull from ipfs to reduce communication impact with the Aggregator
* **Blockchain for Metadata**\
  Critical metadata (e.g., node identity, timestamp, proof-of-work/proof-of-training) is immutably recorded on the blockchain to ensure **auditability** and **traceability**.
* **Global Model Update**\
  The aggregator uses contributed parameters to update the global model, which is then redistributed to all nodes for the next training round.

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### **2. Encrypted Local Device Training Layer (Middle Section)**

This section details **peer-to-peer synchronization** and **private training logic**.

* **Secure Device Alignment**\
  Client nodes (e.g., A and B) perform secure handshakes and model alignment using public-private key encryption.
* **Data Stays Local**\
  No raw data leaves the device. Training occurs on-device using sensitive, high-quality data unavailable to traditional pipelines.
* **Encrypted Model Sharing**\
  Trained model weights are encrypted and uploaded for inclusion in the global model—preserving full data sovereignty.

***

### **3. Secure Aggregation Layer (Right Section)**

This focuses on how the system **safely combines model updates** without compromising privacy.

* **Public Key Exchange**\
  Each node generates and exchanges keys to enable secure gradient exchange.
* **Local Computation**\
  Each device calculates its own gradients, losses, and model deltas privately.
* **Encrypted Aggregation via Aggregator Node**\
  Updates are sent to an Aggregator Node, which aggregates encrypted values and updates the global model without ever seeing individual data.

***

#### **System Highlights**

* ✅ **Zero Raw Data Movement**
* ✅ **Cryptographically Secure Communication**
* ✅ **Full Model Auditability on Blockchain**
* ✅ **Global Coordination, Local Control**

This architecture bridges the gap between **high-quality private data** and **collaborative model development** while offering unmatched transparency and data integrity.



