Discover the power of decentralization for secure analysis while retaining data locality
Federated Learning (FL) is a distributed machine learning (ML) technique that enables model training on data from multiple, decentralized servers with local data samples, without exchanging or moving data. This approach ensures that the data remains in its original location and is not exposed to any other parties.
Another characteristic of FL is that it is typically composed of heterogeneous datasets where the size of the dataset varies from one data owner to another.
Duality developed a second layer of security on top of FL to ensure that the federated computation itself is performed while encrypted, keeping the model concealed from all participants and the computation operation secured.
Distributed computing enables to minimize data transfer, and each data owner to compute on their data locally.
Built-in tools for aligning schemas, transforming data, and pre-processing for multiparty collaboration.
With Federated Learning, data never leaves the premise. The computation is done locally and only partial results are sent to the server.
Ensure full control on your data, decide who accesses the data, which computations will run, at what frequency, and more.
Duality enhance standard Federated Learning frameworks with Fully Homomorphic Encryption, to enable encrypted aggregation of partial results.
Support popular models for training such as linear & Logistic regressions, as well as federated framework for advanced statistics computation,
Improve AI/ML model performance by training, tuning and validating AI and ML models on encrypted data.
Optimize efficiency by working collaboratively across borders while meeting data privacy and residency requirements.
Analyze sensitive medical data across centers to gain deeper insights to speed research and discovery.
Access new high-value data sets in a privacy-protected and compliant manner.
Better understand the genetic bases of diseases and develop appropriate treatments
Apply third-party analytics and ML models to your sensitive data without exposing it.
Deliver personalized customer experiences and upsell with partners while preserving privacy.
Create new revenue sources from sensitive data while preserving privacy and regulatory compliance.
Maximize the value of sensitive, regulated, or confidential data.
