SHERPA.AI
PRIVACY-PRESERVING PLATFORM

The first cloud based Privacy-preserving B2B SaaS platform.

the 3 federated

The 3 Federated Learning paradigms (Horizontal, Vertical and Transfer).

secure

Combined with Privacy-Enhacing complementary technologies (Differential Privacy, homomorphic encryption, among others) that will make our platform the most advanced worldwide.

checked

With an intuitive and user friendly UI that will help to democratize the access to data privacy to a wider spectrum.

UNLOCK NEW SCENARIOS WITH PRIVACY-PRESERVING AI
FEDERATED LEARNING PARADIGMS

horizontal federated

HORIZONTAL FEDERATED LEARNING​

Same type of data in different nodes

vertical federated

VERTICAL FEDERATED LEARNING​​

Different type of data in different nodes

federated transfer

FEDERATED TRANSFER LEARNING​​

The nodes share a few samples of information

schema of federated coming soon
COMING SOON
The only cloud based platform that can support three Federated Learning paradigms combined with Privacy-Enhancing complementary technologies make Sherpa.ai platform the most advanced worldwide.
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ALGORITHMS AND
AI MODEL COVERAGE

FLEXIBILITY AND SCALABILITY.
Adaptability of Sherpa.ai solution to most ML models

The Sherpa.ai Federated Learning platform has been designed to be highly flexible, to adapt to a wide variety of AI algorithms and models. Our platform is ready for user customized models. Furthermore, the Sherpa.ai technology is scalable to a wide range of possibilities, corresponding to different fields of application. Examples of algorithms and ai model coverage are available below.

Content Based Recommender
Deep Neural Networks
Support Vector Machines
Mean Based Recommender
Linear Regression
Decission Trees
Neural Networks
Logistic Regression
K-Means
Bayesian
LG-FedAVG
FedPer
pFedMe
FedProx
Among others

BUILD YOUR MODEL
IN ANY FRAMEWORK

Sherpa.ai platform fits your model.

Its the most flexible and open platform, fully framework agnostic, supporting among others: SK-learn, PyTorch, Tensorflow, Keras and personalized models. Any existent model can be fitted to Sherpa.ai FL platform.

build-your-model

TRAIN WITH ANY
DATA SOURCE

Any Python supported datasource can be used.

By implementing a set of interfaces the node can access the training data on its original sources. File access through local or remote filesystems, relational and no-SQL databases, HTTP data requests... Exploit your data wherever it is stored.

filesystems
file-systems
nosql
No SQL
sql
SQL DB
files
FILES AWS

PLUGABLE FRAMEWORKS
(FAST DEPLOYMENT)

The agility of a platform can be estimated, among other things, by its ability to adapt to existing models. Thus, offering interfaces to existing frameworks is a major competitive advantage.



NODE INSTALLATION BASED ON DOCKER CONTAINERS

ISOLATION

  • A Docker container bundles all the required libraries, making Docker containers totally independent of the underlying operating system.
  • A Docker container defines a predictable environments isolated from other applications.

PERFORMANCE

  • Lightweight images without operating system have much smaller footprints than virtual machines or dedicated servers and can take advantage of existent hardware.

SCALABILITY / REPRODUCIBILITY

  • Environments remains more consistent in Docker; images are easily versioned, making extremely easy the test, roll back and deploy.
  • Straightforward maintenance.

RUN ANYWHERE

  • Docker images are free of environmental limitations, and that makes any deployment consistent, portable and scalable. Containers have the added benefit of running anywhere, providing it is targeted at the OS (Win, Mac OS, Linux, VMs, On-prem, in Public Cloud).
THE MOST FLEXIBLE PLATFORM IN THE MARKET

Train any AI model, build in any AI framework, use any data source

WIDE RANGE OFAGGREGATORS

There are a multitude of aggregation processes, but not all of them are suitable for all models or paradigms neither offer security in untrusted environments or achieve the best accuracy in certain scenarios. A platform must offer a wide range of aggregators with which to approach learning processes of the most diverse nature and ensure that the best result is obtained from the existing model and available data.
Specific aggregators have been designed for Vertical Federated Learning (VFL). One of them is the concatenation aggregator, which, on the one hand, allows to unlock the potential of heterogeneous data; on the pother hand, assisted by Differential Privacy (DP) or Secure Multi-Party Computation (SMPC) preserves data privacy.

The federated architecture itself naturally protect data privacy. However, Adversarial Attacks may be performed to retrieve some information anyhow. Robust aggregators are designed to defend against such attacks, thus enforcing the privacy-resiliency of the federated architecture.

ROBUST AGGREGATORS

Distributed environments architectures for Federated Learning (FL) add vulnerabilities, such as the ability to defend against adversary attacks. In classical machine learning, the majority of adversary attacks can be prevented through data sanitation techniques, where the data is inspected by cleaning it of possible contaminated data. Clearly the constraint that training data never leaves the clients in FL makes it impossible to apply these defense mechanisms and leaves a small margin of maneuver. For this reason, the development of ad-hoc defense mechanisms for FL is crucial.

Robust aggregators provide defenses against poisoning attacks (backdoor and byzantine), avoiding sample missclassification, label flipping, the introduction of samples out of the training distribution and random model updates.

robust aggregators

DIFFERENTIAL PRIVACY
ON TOP OF EVERYTHING

Sherpa.ai platform fits your model.

Its the most flexible and open platform, fully framework agnostic, supporting among others: scikit-learn and TensorFlow. Any existent model can be fitted to Sherpa.ai FL platform.

differential privacy

ARAUDIA

Sherpa.ai-ren ikaskuntza federatuaren ikuspegiari esker, erakundeek datuen potentzial osoa aprobetxa dezakete, horiek partekatu gabe.

Sherpa.ai plataformarekin, kalkulu guztiak eta ereduen prestakuntza datuen jabearen ingurunean gertatzen dira. Horrek asko murrizten du datuen urraketen arriskua eta baita betetze-zama ere. Datuak ez dira inoiz partekatzen, beraz, GDPR, HIPAA HITECH edo CCPA bezalako arauak betetzea bermatuta dago.

Ikuspegi berritzaile honek gaur egun gutxi erabiltzen diren datuen potentziala desblokeatzen du datuen partekatzea mugatzen duen araudiaren ondorioz, baina gaur egun lehiakortasun arrazoiengatik datuak partekatzen ez dituzten arazo komunak dituzten erakundeen arteko lankidetza bide berriak irekitzen ditu.

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Pribatutasuna eta datuen babesa

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

Datuen pribatutasuna oinarrizko balio etikoa da Sherpa.ai-n. Gure plataformak Datuak Babesteari buruzko indarrean dagoen araudi guztia betetzen du (GDPR) eta bat dator Europako Adimen Artifizialaren Europako Batzordearen arau-esparruaren proposamenarekin.

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Segurtasuna

Information security is a top priority at Sherpa.ai. Gure ustez, segurtasunak kalitate estandarrak eta horri buruzko araudi guztiak bete behar ditu. Hori dela eta, ISO-27001 datuen segurtasun-arauan ziurtagiria dugu eta gure plataformak CogX 2021 sariak irabazi ditu Teknologiaren Erregulazioari egindako ekarpen bikainagatik eta Pribatutasun eta Datuak Babesteko Soluzio Onenaren finalista izan da.

Informazioaren segurtasuna lehentasuna da Sherpa.ai-n.

JARRI HARREMANETAN SHERPA.AI-REKIN

Maximize the value of data and AI with Sherpa.ai’s Privacy-Enhancing solutions

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