XAIN is a company that provides federated learning as a service to allow organizations to train AI models on decentralized data without aggregating or anonymizing private user data. Their federated learning platform allows models to be trained separately on each organization's local data and then combines the models for improved accuracy while keeping all data localized. They have successfully worked with companies like Porsche and are hiring various engineering roles.

1. 1
The eXpandable AI Network
Federated Learning
as-a-Service
XAIN solves the privacy dilemma in AI &
makes data anonymization obsolete.

2. 2
Our Founders
Leif-Nissen Lundbæk, CEO
Felix Hahmann, Chairman of Supervisory Board
Prof. Michael Huth, CTO
Who we are
WE ARE PROUD TO HAVE
ALREADY SUCCESSFULLY
WORKED WITH: ...
▷ Degrees in Mathematics, Software Eng. & Economics
▷ Researcher at Oxford University & Imperial College
▷ Computer Science Chair at Imperial College London
▷ Research in Cybersecurity & Privacy in ML
▷ Degree in Applied Computer Science
▷ IT project lead at IBM, Mercedes & EON
Founded in February 2017
Project of researchers at Oxford University and
Imperial College London
Headquartered in Berlin with 30 Employees and
80% engineers
Winners of first Porsche Innovation Contest
XAIN raised so far around 9.5 Mio. € in Seed
capital with Earlybird VC as a lead
Federated Learning platform in production at
Porsche
XAIN in a Nutshell

3. 3
Current Job Openings
Join the Team
Senior Python
Backend Engineer
We are currently looking for
a dedicated and ambitious
Senior Python Backend
Engineer (m/f/d) to join our
team of international
multi-talents at our office in
Berlin Mitte.
Read More >
Senior DevOps
You will be responsible for
the setup and maintenance
of our applications on
Kubernetes, directly control
the configuration,
management and security of
our infrastructure and data.
Read More >
Senior AI Engineer
You will be implementing
robust, production-grade
machine learning services,
which run theoretically
sound and statistically valid
models.
Read More >
Frontend Engineer
We are looking for an
experienced Frontend
Engineer (m/f/d). You are
passionate about building
robust & scalable frontend
applications, which
incorporate design,
functionality as well as
usability across devices and
browsers.
Read More >

4. 4
Model: Training versus Inference
AI Fundamentals
ModelNew Data Prediction
Training Data
Machine Learning Algorithm
TRAINING
INFERENCE

5. 5
AI projects with personal
data: at risk due to
Consumer and Regulatory
Pressure!
Public
perception
↦The future will be defined by strict
anonymization of AI training data -
No personal data will be identifiable.
Regulatory
climate

6. 6
Does data aggregation & anonymization help?
Privacy in AI: The current “Solution”
Model Prediction
Algorithm
New Data
Central Database
Training Data Training Data
Training Data Training Data
Anonymization
+
Aggregation

7. 7
↦The more you anonymize, the less
accurate your AI will be.
Current anonymization for
AI does not work
accuracy
privacy
accuracy
privacy
HOWITISHOWITSHOULDBE
Anonymization means more than removing names.
Valuable data has to be removed, making the
training of personalized AI nearly impossible.

8. 8
Is there a better
way to train AI?
WHAT NOW?

9. 9
Effective and Compliant
Training on non-anonymized
data in its silos
What we want

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Don’t consolidate user data through anonymization,
consolidate trained knowledge!
This approach is called
FEDERATED LEARNING
TRAIN SEPARATELY on every
data silo and COMBINE ALL
models for better model accuracy
How can we get this?

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2
1
3
Federated Learning works in
three steps of continuous
iteration but has limitations
3 | Global Model
Distribution
The global model that XAIN
computes has higher
accuracy and is pushed back
to local training sites.
1 | Local
Training
All local models get
trained in local
customer environment
with local data.
2 | Model
Aggregation
Selected updated local models
are pushed to a platform
instance, which aggregates these
updates into a global model.
Limitations:
- Scalability when it comes to millions of local participants
- Full AI not compliant, as the Global Coordinator sees all local models
11

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Our solution is framework &
model agnostic
↦Developers can install a simple SDK and
simply integrate a few lines of code into
any AI pipeline.
# Dataset
(x_train, y_train),(x_test, y_test) =
tf.keras.datasets.mnist.load_data()
data = (x_train / 255.0, y_test)
# Model
model = Sequential([
Flatten(input_shape=(28, 28)),
Dense(128, activation='relu'),
Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# XAIN
xain.start_participant(model, data)
Solving these FL-Limitations with crypto, AI, & Innovation

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centralized
federated
partitioning grade
accuracy
Max achieved accuracy
for centralized and
federated learning Substantial Model
Accuracy Gains
Benchmarking Results
⭑ Substantial gain offered by Federated Learning
over centralized ML
⭑ That gain is even greater if the data is
distributed in a very uneven fashion
1) IID: independent and identically distributed. Refers to same class data (e.g. accounting data, engine test data, etc.)

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We monetize our Open Source
tech stack through a
Self-Service Platform
The XAIN Fully Managed Platform provides features to easily
integrate, monitor and manage Federated Learning projects.
↦Pricing is based on the number of integrated
SDKs and AI model sizes.
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XAIN supports server
based and even edge
based models (mobile)
Under the California Consumer Privacy Act,
insurances cannot continue its current data
evaluation process for telematic insurances.
↦With XAIN, insurance providers can even
train risk models directly on user phones
and federate the learning.
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jobs@xain.io
@XAIN_AG
XAIN AG
Unter den Linden 42
10117 Berlin, Germany
https://xain.io
You are interested
in working with us?
Just get in touch! Beatrice Kahl
Head of HR