6:22:28 AM PDT - Tuesday, October 4th, 2022

DynamoFL aims to bring privacy-preserving AI to more industries  

By Editor - Tue Sep 20, 4:09 am

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Data privacy regulations like GDPR, the CCPA and HIPAA present a challenge to training AI systems on sensitive data, like financial transactions , patient health records and user device logs. Historical data is what “teaches” AI systems to identify patterns and make predictions, but there are technical hurdles to using it without compromising a person’s identity.  One workaround that’s gained currency in recent years is federated learning. The technique trains a system across multiple devices or servers holding data without ever exchanging it, enabling collaborators to build a common system without sharing data. Intel recently partnered with Penn Medicine to develop a brain tumor–classifying system using federated learning, while a group of major pharma companies, including Novartis and Merck, built a federated learning platform to accelerate drug discovery. Tech giants, including Nvidia (via Clara ), offer federated learning as a service. But a new startup, DynamoFL , hopes to take on the incumbents with a federated learning platform that focuses on performance, ostensibly without sacrificing privacy. “ DynamoFL was founded by two MIT Department of Electrical Engineering and Computer Science PhDs, Christian Lau and myself, who spent the last five years working on privacy-preserving machine learning and hardware for machine learning,” CEO Vaikkunth Mugunthan told TechCrunch in an email interview.

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DynamoFL aims to bring privacy-preserving AI to more industries

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