FedZero
Network
Collaborative machine-learning training across edge devices without sharing private datasets. Computations are verified by zero-knowledge proofs on Arbitrum and incentivized on Solana.
AI MUST LEARN FROM DATA WITHOUT STEALING IT.
Traditional AI requires organizations and individuals to surrender raw proprietary datasets into centralized cloud warehouses (OpenAI, AWS, Google Cloud). This creates dangerous single points of failure, copyright litigation, and catastrophic medical and financial privacy leaks.
The Solution: Bring Code to Data, Not Data to Code
The FedZero Network unites Federated Learning (FL) with Zero-Knowledge Cryptography (zkML) and a Tripartite Multi-Chain Architecture. Devices independently compute local parameter weight improvements without ever transferring private data. Zero-Knowledge proofs verify computation integrity on-chain, eliminating the need to blindly trust edge participants.
Federated Learning (FedAvg)
Participants download the latest global model tensor, perform local epochs on edge datasets, and return mathematical delta weights (ΔW). Distance-based outlier rejection and L2 norm clipping actively defend against adversarial poisoning.
- ✔Cross-device & Cross-silo FL
- ✔Byzantine outlier filtering
Zero-Knowledge Proofs (zkML)
Eliminates the "lazy node" problem. Every edge participant synthesizes an EZKL / Halo2 SNARK proof verifying that the claimed forward and backward training steps were honestly calculated without falsified weights.
- ✔Halo2 KZG & BN254 circuits
- ✔Quantization constraint checking
Tripartite Multi-Chain Layer
Arbitrum verifies Groth16/KZG pairings and tracks model lineage CIDs. Solana delivers high-throughput token rewards. Zcash serves as the zero-knowledge privacy benchmark.
- ✔ZKVerifier.sol & AIPassport.sol
- ✔Dynamic reward scoring engine
HOW A FEDERATED TRAINING ROUND OPERATES
Local Edge Device Training
Raw datasets (medical records, financial transactions, private user actions) remain strictly confined to the participant's hardware. Models train locally for E epochs to generate gradient parameter updates (ΔW).
W_i^(t+1) = W^(t) − η · ∇L_i(W^(t); D_i)Har node apne private data (D_i) par error gradient calculate karta hai aur weights update karta hai — kisi bhi patient ya user ka raw data device se bahar nahi jata.
TRADITIONAL AI VS. VERIFIABLE NETWORK
How our zero-knowledge federated protocol changes the fundamental trust assumptions of machine learning.
| Evaluation Vector | Traditional Centralized AI | FedZero Protocol (This Project) |
|---|---|---|
| Raw Data Custody | Uploaded to corporate servers; vulnerable to breach. | 100% Client-Side; raw data never leaves device hardware. |
| Computation Trust | "Trust us" cloud provider guarantees; no proof of honesty. | Halo2 zk-SNARK cryptographic validity proof (π). |
| Model Version Lineage | Opaque internal release notes; easily modified or retracted. | Arbitrum Smart Contract (ModelRegistry.sol) with IPFS CIDs. |
| Contributor Incentive | Big tech monopoly captures 100% of commercial value. | Automated Solana micro-rewards based on quality & compute. |
| Reputation & Sybil Defense | Centralized ban-lists and closed API tokens. | Soulbound AIPassport.sol tokens & rate-limited PDAs. |
READY TO INTERACT WITH THE RUNNING NETWORK?
Launch the live telemetry dashboard. Trigger federated learning training rounds, inspect Halo2 zkML constraints, manage autoML datasets, and view automated Solana rewards in real time.