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How TripleBlind Compares To Federated Learning

Federated Learning is a learning paradigm that allows multiple parties to collectively train a global model using their decentralized data without the need to centrally store it; and, thus, without the need to transmit it outside the owner’s infrastructure.

Google coined the term Federated Learning in 2016, and the company has since been at the forefront of AI training through this method. From a high level of abstraction, Federated Learning goes through the following steps:

  • A central server chooses an algorithm or statistical model to be trained. The server transmits the model to several data providers, often referred to as clients (consumers, devices, companies, etc.);
  • Each client trains the model on their data locally and shares updates with the server;
  • The server receives model updates from all clients and aggregates them into a single global model. The most common aggregation approach is averaging.

Federated Learning has the opportunity to be beneficial in both healthcare and financial markets, with the potential to create unbiased medians of large amounts of consumer information. In healthcare, trained models via Federated Learning can help with diagnosing rare diseases based on other patient data. In fintech, Federated Learning allows institutions to detect crime and risk factors within their collaboration network. 

Federated Learning only accesses the results and learnings based on the algorithms, which are then sent back to the server without sharing the actual data. It is meant to keep individual consumer data private. However, while Federated Learning allows for more privacy than has previously been possible with AI, there are downfalls when it comes to the model privacy and efficiency of collaboration through Federated Learning. 

Because Federated Learning requires each of the clients to train the model on their entire dataset locally, there is both a high computational load and high communication overhead.

When multiple parties collaborate through Federated Learning, the model through which the collaboration takes place is known to everyone involved, making it susceptible to several attacks that could lead to data leakage. Moreover, it also puts the actual model privacy at risk.

TripleBlind’s Blind Learning approach is superior to and more efficient than Federated Learning and offers a more secure and precise way to share data. With TripleBlind’s groundbreaking solution, de-identified data is shared through models in which TripleBlind and all other parties involved are blind to the model and the original data.


reconstruction attack example image

This comparison shows how private data shared via TripleBlind’s solution remains private and de-identified in the case of a data breach


Data sets are shared so that only information relevant to the collaboration can be used, and only for the intended purpose. By preventing reconstruction attacks, TripleBlind ensures there is no risk of the data ever being re-identified if there were to be a data breach.

We are comparing TripleBlind’s technology to other modes of data collaboration as part of our Competitor Blog Series. Stay up to date with TripleBlind on Twitter and LinkedIn to learn more. If you’re interested in knowing more about how collaborating using TripleBlind’s patented solution can safely and efficiently unlock privacy for you, please email for a free demo.

Read the other blogs in this series:
Business Agreements
Homomorphic Encryption
Synthetic Data
Tokenization, Masking and Hashing
Differential Privacy