Federated
learning

After years of research, Sherpa.ai has develop the most advanced Federated Learning Platform for data privacy, which is making a huge impact on the academic world and industry.

Federated Learning is a Machine Learning paradigm aimed at learning models from decentralized data, such as data located in users’ smartphones, hospitals, or banks, and ensuring data privacy.

This is achieved by training models locally at each node (e.g. at each hospital, at each bank or at each smartphone), sharing the model-updated local parameters (without sharing user data) and securely aggregating them to build a better global model.

PRIVACY-PRESERVINGAI PLATFORMALLPERSONAL DATABUSINESS 1BUSINESS 1BUSINESS 2BUSINESS 2BUSINESS 3BUSINESS 3… BUSINESS N… BUSINESS NPERSONAL DATAPERSONAL DATAPERSONAL DATAPERSONAL DATAALL DATA IN ONE PLACETRAIN A DEEP LEARNING ALGORITHM ON THE AGGREGATED DATASETDATA STAYS LOCALTRAIN A MACHINE LEARNING ALGORITHM LOCALY WHILE PRESERVING DATA PRIVACY AND BEING COMPLIANT WITH REGULATIONSPERSONAL DATAPERSONAL DATAPERSONAL DATAPERSONAL DATANO PERSONAL DATA EXCHANGEDFLFLFLFLPERSONAL DATAEXCHANGED

Where to apply
Federated Learning

This technology is disruptive in cases where it is mandatory to guarantee data privacy.

When the data contains confidential information, such as private patient data, personal financial information and any other confidential information.

Due to data privacy legislation, healthcare institutions, banks and insurance companies, for example, cannot share individual records, but would benefit from AI training in machine learning models from data from various entities.

Two parties want to take advantage of their data without sharing it. For example, two insurance companies could improve fraud detection, training models through federated learning, so that both companies would have a highly accurate predictive algorithm, but at no time would they share their business data with the other party.

Benefits

More powerful collaborative models, or not feasible using standard solutions, without exchanging private data.

Data privacy by design, without risk of being compromised.

Regulation Compliance. The data never leaves the environment of the parties involved.

Lower risk of data breaches. The attack surface is reduced.

Transparency about how models are trained and how data is used.

Types of federated learning

The Sherpa.ai platform supports different types of Federated Learning: Horizontal, Vertical and Federated Transfer Learning. Being able to train models in very diverse scenarios and use cases.

Federated Learning types
Federated Learning types

Other technological aspects
of sherpa.ai´s privacy-preserving technology

Secure Multi-Party
Computation

Secure Multi-Party Computation (SMPC) is a cryptographic protocol that distributes the computation of data from different sources to ensure that no one can see the information of the others, without even the need to trust a third-party.

Privacy preserving aspects

Differential
Privacy

Differential Privacy is the statistical technique to ensure that no malicious agent can be able to trace back any individual information back to the data source. At Sherpa.ai we are able to achive this by injecting noise with Laplacian, Gaussian or Exponential mechanisms. This makes personal information untraceable, ensuring that no individual data can be 'reverse engineered' while maintaining the usefulness and relevance of prediction algorithm.

Privacy preserving aspects

SHERPA.AI Federated Learning
open source framework

Sherpa.ai has made available to universities, research centers, companies and developers an open source framework that allows to experiment and develop Artificial Intelligence solutions, respecting privacy using Federated Learning and Differential Privacy.

Go to Developers

We have reached the highest levels in the implementation of algorithms for the Artificial Intelligence platform with data privacy of Sherpa.ai, with the most advanced methodologies of applied mathematics

Imagen de Enrique Zuazua

Enrique Zuazua, Ph.D.

Senior Associate Researcher in Algorithms of Sherpa.ai

  • Chair Professor at FAU (Germany)
  • Alexander von Humboldt Award.
  • Considered as the world's best one in applied mathematics

It is the most powerful platfom on the market that respects user privacy, based on cutting edge Federated Learning Technology

Imagen de Francisco Herrera

Francisco Herrera, Ph.D.

Senior Associate Researcher on Machine Learning of Sherpa.ai

  • National Computer Science Award
  • More than 331 research Articles
  • More than 100.000 citations in Google Scholar

Compliance

Logo GDPR

Privacy

Data privacy is a fundamental ethical value at Sherpa.ai.

Our platform complies with all current regulations on Data Protection (GDPR) and is in line with the European Commission regulatory framework proposal on Artificial Intelligence.

Logo small CogX 2021 winner
Logo small CogX 2021 finalist
Logo GDPR

Security

Information security is a top priority at Sherpa.ai.

We believe that security must comply with quality standards and with all regulations in this regard. For this reason, we are certified in the ISO-27.001 data security standard and our platform has won the CogX 2021 awards for its Outstanding Contribution to Technology Regulation and has been a finalist as Best Solution for Privacy and Data Protection.

CONTACT SHERPA.AI

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