Decentralized data is hard to use for machine learning.
Compliance with privacy and data residency regulations requires data to remain within its jurisdiction or organization.
Contractual limitations prevent companies from moving data or utilizing it outside of pre-defined use cases.
Physical data silos within an organization require complex technical solutions to centralize.
Data sharing agreements are difficult to write and challenging, time-consuming, and costly to execute.
PowerFlow makes it easy to train models with decentralized data and capture new machine learning opportunities.
Infrastructure and simple management tools reduce implementation costs and timelines.
Differential privacy settings equip you with tools to understand and protect data and model privacy.
Model performance tools
Train performant models even with zero visibility into raw data.
Model training reports help you understand which data sets add value to your model.
What is Federated Learning?
Unlike traditional machine learning techniques that require data to be centralized for training, federated learning is a method for training models on decentralized datasets. Portions of a machine learning model are trained where the data is located (e.g., these could be private datasets from two or more companies) and model parameters are shared among participating siloed datasets to produce an improved model. No data moves within the system, which means that organizations can collaborate without compromising privacy or sensitive IP while avoiding the pain and expense of transferring data through traditional means.