We architect and deploy custom smart contracts for DeFi, NFTs, and enterprise applications. Our development process ensures security-first design, gas optimization, and comprehensive testing before mainnet deployment.
LABS
Services
Federated Learning Data Storage Network
We design and implement secure, partitioned storage protocols specifically for federated learning workflows. Enable multiple parties to collaboratively train AI models without exposing raw, centralized data sets.
Chainscore © 2026
overview
CORE SERVICE
Smart Contract Development
Secure, production-ready smart contracts built by Web3 experts to power your protocol.
We deliver battle-tested code that passes rigorous audits, reducing your time-to-market and technical risk.
- Protocol Development: Custom
ERC-20,ERC-721,ERC-1155, and bespoke token standards. - DeFi & dApps: Automated Market Makers (AMMs), lending/borrowing pools, staking mechanisms, and governance systems.
- Security & Audits: Built with
OpenZeppelinlibraries and subjected to internal review + third-party audit readiness. - Full Lifecycle: From architecture and development to deployment, verification, and ongoing maintenance.
key-features-cards
ARCHITECTURE
Core Protocol Features
Our federated learning data storage network is engineered for enterprise-grade privacy, scalability, and compliance. We deliver the foundational infrastructure so your team can focus on model innovation.
benefits
TANGIBLE RESULTS
Business Outcomes for Your AI Project
Deploying a federated learning network requires more than just infrastructure; it demands a partner who delivers measurable business value. We architect your data storage network to achieve specific, quantifiable outcomes.
01
Accelerated Model Training Cycles
Reduce federated training rounds by up to 40% with our optimized data orchestration and peer-to-peer communication layer, getting your AI models to production faster.
40%
Faster Training
< 2 weeks
Network Setup
02
Enterprise-Grade Data Sovereignty
Maintain full control and compliance (GDPR, HIPAA-ready) with our privacy-by-design architecture. Data never leaves the client device; only encrypted model updates are aggregated.
Zero-Trust
Data Model
E2E Encrypted
All Updates
03
Drastically Lower Infrastructure Costs
Eliminate centralized data lake costs and reduce cloud compute spend by leveraging a decentralized network. Pay only for verification and aggregation compute.
60-80%
Cost Reduction
Pay-per-Use
Pricing Model
04
Provably Fair & Transparent Incentives
Attract high-quality data contributors with our cryptographically verifiable incentive mechanism. Ensure model quality and network participation with smart contract-based rewards.
On-Chain
Reward Distribution
Automated
Quality Scoring
05
Battle-Tested Security & Resilience
Protect against model poisoning and Sybil attacks with our multi-layered security framework, including cryptographic proofs and decentralized validation committees.
99.9%
Uptime SLA
Formally Verified
Core Protocols
06
Seamless Integration & Scalability
Integrate with your existing ML pipelines (PyTorch, TensorFlow) via our SDK. Scale from thousands to millions of edge nodes without re-architecting your core infrastructure.
1M+
Node Capacity
Standard SDKs
Easy Integration
Infrastructure Decision Analysis
Build vs. Buy: Federated Learning Storage
A detailed comparison of the resource investment required to build a secure, compliant federated learning data storage network in-house versus leveraging Chainscore's managed service.
| Critical Factor | Build In-House | Chainscore Platform |
|---|---|---|
Time to Production | 9-18 months | 4-8 weeks |
Initial Development Cost | $300K - $800K+ | $50K - $150K |
Core Team Required | 5-10 Senior Engineers | 1-2 Integration Engineers |
Security & Compliance Audit | External Firm ($50K-$100K) | Included & Certified |
Cross-Chain Data Provenance | Custom Implementation | Native Feature |
Ongoing Infrastructure Ops | DevOps Team + Cloud Costs | Managed Service SLA |
Model Update Latency | Variable (Days to Weeks) | < 24 Hours Guaranteed |
Total Cost of Ownership (Year 1) | $500K - $1.2M+ | $120K - $300K |
Data Privacy Compliance (GDPR/CCPA) | Your Legal Liability | Our Contractual Guarantee |
how-we-deliver
PROVEN METHODOLOGY
Our Development & Integration Process
A structured, four-phase approach to integrate privacy-preserving federated learning into your data infrastructure, ensuring security, compliance, and operational readiness from day one.
01
Architecture & Protocol Design
We design your custom federated learning network architecture, selecting optimal consensus mechanisms (e.g., Proof-of-Stake for validator nodes) and data sharding strategies. This phase establishes the cryptographic foundation for secure, multi-party computation.
2-3 weeks
Design Phase
Zero-Trust
Architecture Model
02
Smart Contract & Node Development
Development of on-chain coordination contracts for node incentives, data provenance, and model aggregation logic. We deploy and configure secure validator and client nodes with TLS encryption and hardware security modules (HSM) where required.
Solidity 0.8+
Contract Standard
HSM Support
Node Security
03
Privacy-Preserving Integration
Integration of cryptographic libraries for secure multi-party computation (MPC) or homomorphic encryption. We implement client-side SDKs for seamless data contribution and ensure end-to-end encryption, guaranteeing raw training data never leaves the data owner's environment.
MPC / HE
Privacy Tech
GDPR-ready
Compliance Built-in
04
Deployment & Ongoing Governance
We manage the mainnet deployment of your federated network, establish monitoring for node health and model performance, and implement upgrade mechanisms for smart contracts. Includes documentation and training for your team.
99.5% SLA
Node Uptime
24/7 Monitoring
Operational Support
security
CORE SERVICE
Smart Contract Development
Secure, production-ready smart contracts built for scale and compliance.
We architect and deploy custom smart contracts that power your core business logic. Our development process is built on security-first principles and delivers audit-ready code from day one.
- Protocols:
ERC-20,ERC-721,ERC-1155,ERC-4626, and custom standards. - Security: Formal verification, OpenZeppelin libraries, and comprehensive unit/integration testing.
- Frameworks: Hardhat and Foundry for rapid development and deployment.
Reduce your time-to-market with a 2-week MVP delivery and a clear path to mainnet launch.
We manage the full lifecycle:
- Design & Specification: Collaborative sessions to define logic, gas optimization, and upgrade paths.
- Development & Testing: Iterative builds with 99%+ test coverage and simulated attack vectors.
- Deployment & Verification: Mainnet deployment with multi-sig setup and public source code verification.
Technical & Commercial Insights
Federated Learning Storage FAQs
Get clear, specific answers to the most common questions about implementing a secure, decentralized data storage network for federated learning.
A production-ready, custom federated learning data storage network typically deploys in 4-8 weeks. This includes architecture design, smart contract development, node setup, integration with your ML pipeline, and initial testing. For a standardized, pre-configured network solution, deployment can be as fast as 2-3 weeks. The timeline is fixed-price and agreed upon in the project scope.
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