AI Workloads
Privacy-Preserving Model Training
CASE STUDY
Problem:
AI models often require sensitive data (health, financial, biometric). Running them on centralized servers risks surveillance and leaks.
Pinera Solution:
Deploy AI/ML training workloads on decentralized compute clusters.
Encrypt datasets with ZK-proofs for verification without exposing raw data.
Use Pinera’s networking for secure federated learning across global nodes.
Outcome:
Enterprises train AI models with confidential data safely.
AI developers can monetize models without giving up user privacy.
Research groups collaborate securely without central trust.
More Case studies
Common Questions
Answered
Pinera is built to answer some of the most important questions about the future of cloud infrastructure. As a privacy-first DePIN platform, it merges storage, compute, and networking into one unified stack creating a decentralized alternative to AWS, GCP, and other centralized hyperscalers.
