Help Us Unlock Global Federated Learning Infrastructure

We’re building SoraChain AI — a decentralized layer for trustless collaboration and scale FL adoption.

We’re reaching out to pioneers like you to validate key assumptions that could shape the future of decentralized AI. We believe FL is the right way to build and scale the next AI systems.

Estimated completion time: 2–3 minutes.

What best describes your role or affiliation?

Researcher (Academic)

Researcher (Industry)

Are you currently involved in a Federated Learning (FL) project?

Yes – in production

Yes – in research

Which domain is your FL work focused on?

Healthcare

Education / Academia

Cybersecurity / Privacy

Which FL framework(s) are you using or exploring?

Flower

Technical Implementation & Evaluation

How do you currently ensure data privacy and security in your FL system?

Differential privacy

Secure multi-party computation (SMPC)

Trusted execution environments (e.g., Intel SGX)

Blockchain for auditability / provenance

Rely on institutional or regulatory protections

What are the primary technical challenges you’ve encountered when implementing FL? (select up to 3)

Communication overhead / bandwidth constraints

Data heterogeneity across clients

Device heterogeneity (e.g., compute/memory variation)

Privacy risks or regulatory barriers

Difficulty achieving model convergence

Integration with existing infrastructure

Lack of reproducibility / auditability

How do you currently measure the performance and effectiveness of your FL system?

Model accuracy or validation performance

Communication efficiency (e.g., rounds, bandwidth)

Scalability to large clients / geographies

Strength of privacy guarantees

Fairness across participants

Hardware/Compute efficiency

Challenges in FL Collaboration

What are the biggest constraints you currently face in your FL system or workflow?

Difficulty scaling across institutions

Difficulty managing model versioning across nodes

Communication latency or unreliable edge devices

Security concerns during aggregation

Lack of incentive mechanisms for participation

Lack of well defined business prospectives

Limited support for heterogeneity (devices/data)

Compliance with privacy laws (e.g., GDPR, HIPAA)

Have you attempted or considered institutional FL collaboration?

Yes – successfully implemented

Yes – faced challenges

No – due to constraints

No – not applicable

What are major inherent challenges for scaling FL collaboration?

Data privacy / regulations

No coordination infrastructure

Technical incompatibility

Lack of incentives

Infrastructure & Blockchain

To what extent do you agree:

A blockchain-based infrastructure could enable scalable, transparent, and secure FL global collaboration.

(Linear scale 1–5: Strongly Disagree → Strongly Agree)

What features would you find most essential in a decentralized FL infrastructure?

Transparent audit trails

Trustless Collaboration

Decentralized governance

Built-in privacy tools (e.g. TEEs, differential privacy)

Would a decentralized coordination layer help you scale and collaborate to build more effective AI Models?

Yes

Curious to experiment

Would you be open to participating in a pilot program?

Yes