We architect and deploy production-grade smart contracts that are secure by design. Our process integrates formal verification and multi-stage audits before any code touches the mainnet.
LABS
Services
Privacy-Preserving Federated Learning for Mobile DePIN
We design and implement federated learning protocols integrated with mobile DePIN networks, enabling hotspots to train AI models on-device. Raw user data stays private, and model updates are settled on-chain for verifiable collaboration.
Chainscore © 2026
overview
CORE SERVICES
Smart Contract Development
Secure, audited smart contracts built to your exact specifications by Web3-native engineers.
Deliver a market-ready, secure contract suite in as little as 2-4 weeks, from spec to deployment.
- Custom Logic: Build for
ERC-20,ERC-721,ERC-1155, DAOs, DeFi protocols, and bespoke business rules. - Security First: Development follows OpenZeppelin standards, with pre-audits using Slither and MythX.
- Full Lifecycle: We handle deployment, upgradeability patterns (
TransparentProxy/UUPS), and post-launch monitoring.
key-features-cards
ENTERPRISE-GRADE PRIVACY
Core Technical Capabilities We Deliver
We build production-ready, privacy-preserving federated learning systems that enable you to train ML models on sensitive data without centralizing it, unlocking new revenue streams while maintaining strict compliance.
01
Secure Multi-Party Computation (MPC) Orchestration
Deploy MPC protocols for secure aggregation of model updates. We implement battle-tested frameworks like PySyft and custom MPC circuits to ensure no single party ever sees raw training data.
End-to-End
Privacy Guarantee
GCP/AWS/Azure
Cloud Agnostic
EXPLORE
benefits
DELIVERABLES
Business Outcomes for DePIN Builders
Our privacy-preserving federated learning service delivers concrete, measurable results for DePIN projects, from faster model convergence to secure, compliant data collaboration.
01
Accelerated Model Development
Reduce model training cycles by up to 40% using our optimized federated averaging protocols and secure aggregation techniques, enabling faster iteration and time-to-market for your AI-powered DePIN applications.
40%
Faster Training
< 2 weeks
Integration Time
02
Regulatory & Data Compliance
Deploy with confidence. Our architecture is designed for GDPR, CCPA, and other data sovereignty regulations, using cryptographic proofs and differential privacy to ensure user data never leaves its source.
Zero-Trust
Data Model
GDPR/CCPA
Compliant
03
Scalable Network Participation
Onboard thousands of heterogeneous edge devices (IoT sensors, smartphones, routers) into your federated network with our lightweight client SDKs and managed node orchestration.
10k+
Device Support
99.5%
Uptime SLA
04
Provable Data Integrity & Audit
Generate immutable, on-chain attestations for model updates and participant contributions using zk-SNARKs. Provide transparent proof of fair, uncorrupted training processes to stakeholders and auditors.
zk-SNARKs
Verification
On-Chain
Audit Trail
05
Reduced Operational Overhead
Eliminate the complexity of building and securing federated learning infrastructure. We handle node management, secure aggregation, failure recovery, and version updates so your team can focus on core logic.
90%
Ops Reduction
24/7
Managed Service
06
Monetization & Incentive Engine
Integrate customizable token incentive models to reward data contributors based on the quality and quantity of their participation, powered by smart contracts for transparent and automatic payouts.
Custom
Tokenomics
Auto-Settlement
Payouts
Structured Implementation Roadmap
Phased Development & Integration Packages
Compare our modular service tiers for integrating privacy-preserving federated learning into your Web3 application, from initial PoC to full-scale production.
| Feature / Deliverable | Proof-of-Concept (PoC) | Production-Ready | Enterprise Scale |
|---|---|---|---|
On-Chain Aggregation Smart Contract | |||
Custom MPC/TEE Node Setup | Basic (1-2 nodes) | Full Cluster (3-5 nodes) | Geo-distributed Cluster (5+ nodes) |
Privacy Protocol Integration | Single (e.g., ZK-SNARKs) | Dual (ZK + TEE) | Multi-Protocol Orchestration |
Client SDK & API | Limited Functions | Full-featured SDK | White-labeled SDK & Docs |
Model Training Pipeline | Basic Docker Setup | Managed Kubernetes | Fully Managed Service |
Security Audit Scope | Core Contract Only | Full Stack | Full Stack + Pen Test |
SLA & Uptime Guarantee | 99.5% | 99.9% | |
Support & Maintenance | Slack Priority | 24/7 Dedicated Engineer | |
Implementation Timeline | 4-6 weeks | 8-12 weeks | 12-16 weeks |
Starting Price | $50K | $150K | Custom Quote |
how-we-deliver
PROVEN METHODOLOGY
Our Development & Integration Process
A structured, four-phase approach designed to deliver production-ready, privacy-preserving ML models with minimal disruption to your existing workflows.
01
Architecture & Data Strategy
We analyze your data landscape and define the federated learning architecture. This includes selecting the optimal privacy framework (e.g., Differential Privacy, Secure Multi-Party Computation) and designing the data partitioning strategy for your specific use case.
1-2 weeks
Design Sprint
100%
Privacy Blueprint
02
Model & Protocol Development
Our team develops the core federated learning algorithms and the secure aggregation protocol. We implement the on-chain/off-chain coordination logic using frameworks like PySyft or TensorFlow Federated, ensuring model integrity and participant privacy.
Solidity 0.8+
Smart Contracts
OpenZeppelin
Security Patterns
03
Secure Node Deployment
We provision and configure the secure compute nodes for each data silo. This includes setting up encrypted communication channels, implementing access controls, and deploying the client-side model training containers in your chosen environment (cloud, on-prem, or hybrid).
TLS 1.3
Encryption
Zero-Trust
Network Model
04
Integration & Ongoing Operations
We integrate the federated learning system with your production data pipelines and MLOps stack. We provide comprehensive monitoring dashboards for model performance, data contribution, and system health, along with SLAs for uptime and support.
99.9%
Uptime SLA
24/7
Monitoring
security-approach
FULL-STACK INFRASTRUCTURE
Custom Blockchain Development
Build, deploy, and scale custom blockchain networks tailored to your business logic.
We architect and implement bespoke blockchain solutions from the ground up, moving beyond generic frameworks. Our full-cycle development delivers production-ready networks optimized for your specific use case—whether it's a private consortium chain, a high-throughput L2, or a custom application-specific chain.
From initial design to mainnet launch, we own the entire stack to ensure security, performance, and seamless integration.
- Protocol Design: Custom consensus mechanisms (
PoA,PoS), tokenomics, and governance models. - Core Development: Rust/Solidity-based node clients, RPC layers, and cross-chain bridges.
- Deployment & DevOps: Automated CI/CD, node orchestration, and 99.9% uptime SLA monitoring.
- Enterprise Integration: Secure APIs, legacy system connectors, and compliance tooling.
Technical & Commercial Considerations
Federated Learning for DePIN: Key Questions
Answers to the most common questions CTOs and product leads ask when evaluating privacy-preserving federated learning for their DePIN infrastructure.
We implement a secure, multi-party computation framework tailored for DePIN data. The process is: 1) On-device model initialization on edge nodes (IoT, sensors, routers). 2) Differential privacy applied to local model updates before transmission. 3) Secure aggregation via cryptographic protocols (like SecAgg) on our orchestration layer. 4) Global model update distributed back to nodes. We use PySyft or TensorFlow Federated, containerized for your hardware, ensuring no raw data ever leaves the device.
ENQUIRY
Get In Touch
today.
Our experts will offer a free quote and a 30min call to discuss your project.
NDA Protected
Confidentiality24h Response
Time to ValueDirectly to Engineering Team
No Sales Fluff10+
Protocols Shipped
$20M+
TVL Overall