Federated Learning With SymetryML

Federated Learning, an emerging area of AI and often referred to as "Collaborative Learning", is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers. The actual data is never shared or moved, instead only "summarized learnings" of models (or in SymetryML's case - the PSR) are shared amongst members of a federation through SymetryML's encrypted solution. This means knowledge can be shared without the hassle or security risk of aggregating sensitive data into a central server.

Traditional Data Sharing

Traditionally, companies have had to aggregate or pool data into a central server (or data lake) in order to leverage all the data for model building and analysis. This is time consuming, costly and increases risk if cyber attack occurs (all data is one location). Federated learning eliminates all of these issues.

Federated Learning - Example A

This is a federation where each node will share its PSR with other nodes, so that all participants can benefit from using all the data, but each node handles model building, deployment and maintenance on its own.

Federated Learning - Example B

This federation represents a scenario where all nodes share their respective PSR with a central group (internal or external) that can handle the model building, deployment and maintenance.

SymetryML's Federated Learning Environment brings several unique opportunities & benefits:

  • Encrypted AI

    Data privacy concerns often limit the full potential of analytics or predictive modeling, as companies don't want to expose or share sensitive data. With the privacy-preserving nature of federated learning, companies can now fully unlock the potential of all of data - regardless of its sensitive nature - as data is "shared" via an encrypted statistical representation (The PSR).

  • Scalable Enterprise AI

    In large organizations, analytics and machine learning are often done in silos, in large part because of the hassle and risk of aggregating massive amounts of data and keeping that data or models up-to-date. Federate Learning allows organizations to streamline their AI operations by eliminating the need to aggregate data for collaborative machine learning and analytics.

  • A New Era of Aggregate Knowledge

    Effective machine learning requires lots of data to learn from, and in cases like fraud or anomaly detection, organizations may not have enough of the right type of data to produce an effective model. Federated Learning creates an opportunity for companies to partner with each other in order to “share” data to create more effective solutions of their own.


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