Federated Data networks to boost collaboration across silos
Sherpa.ai’s Federated Learning platform enables organizations to collaboratively train global AI models across distributed data sources — without ever exposing sensitive information. Each data silo retains full control and privacy, while the resulting models can be securely and privately deployed at the edge for real-time, local decision-making.
Foster collaborations without sharing data
In most real-world scenarios, data is distributed across decentralized silos — such as users’ smartphones, hospitals, or financial institutions — where strict privacy regulations or operational constraints prevent data sharing, making collaboration virtually impossible. Sherpa.ai overcomes this barrier by enabling collaboration to build and train AI models without ever moving or exposing sensitive data.
With Sherpa.ai models are trained locally at each node (e.g., each hospital, bank, or device), sharing only encrypted model updates — not raw data — which are then securely aggregated to create a stronger, more accurate global models. Once trained, these models can be privately deployed at the edge, ensuring real-time decision-making while maintaining full data sovereignty and compliance.
Traditional Approach – Centralized Data Sharing
Conventional machine learning requires aggregating all training data into a centralized location before training can start. An approach that introduces significant roadblocks such us regulation or risks, especially when handling sensitive information.
Sherpa.ai Solution – Data Stays Local
Sherpa.ai’s Federated Learning platform sends models to where data lives—like hospitals or smartphones—for local training. Only model updates are shared back, keeping raw data private and secure while enabling global collaboration.
Sherpa.ai enables collaboration when data sharing is not an option
Our solution has transformative potential where data sharing limitations apply. Organizations encounter data sharing limitations from different reasons including regulation, asset protection or data ethics among others.
Regulation
Laws like GDPR or HIPAA prohibit moving sensitive data across borders or systems.
This is specially present in heavily regulated sectors like Financial Services or Healthcare.
Security
When data is siloed or classified, it can’t be shared without risking breaches or policy violations.
This is specially present when dealing with highly sensitive or classified information.
Infrastructure
Limited bandwidth or storage in edge devices makes transferring large datasets impractical or slow.
This is specially present when dealing with edge devices with limited bandwidth or resources.
Benefits of Sherpa.ai's Federated Learning Platform
Our solution can drastically improve results within weeks while ensuring the highest standards of privacy and security.
Enable collaboration across distributed data silos
Guarantee that no data is ever shared or exposed
Ensure complete regulatory compliance
Enhanced privacy and security
Plug & Play deployment
Integration with any tech stack
FAQs
What is Federated Learning?
What is the difference between horizontal and vertical Federated Learning?
Is Federated Learning compliant with regulation? (GDPR / HIPAA)
What are the benefits of Federated Learning vs other technologies?
Federated Learning vs Centralized Data
Federated Learning vs Data Anonymization
Federated Learning vs Synthetic Data Generation
Federated Learning vs Data Clean Rooms
Federated Learning vs Homomorphic Encryption