Federated Learning and Privacy Preserving AI Course

Explore Federated Learning techniques and privacy-preserving AI approaches to enhance security in data processing and machine learning applications.

Artificial Intelligence and Machine Learning Courses Confirmed

Training Locations

This Federated Learning and Privacy Preserving AI Course is available in multiple cities. Please select your preferred location from the list below

Durrës

Albania

Tirana

Albania

Andorra la Vella

Andorra

Escaldes-Engordany

Andorra

Innsbruck

Austria

Salzburg

Austria

Vienna

Austria

Gomel

Belarus

Minsk

Belarus

Antwerp

Belgium

Brussels

Belgium

Banja Luka

Bosnia and Herzegovina

Sarajevo

Bosnia and Herzegovina

Plovdiv

Bulgaria

Sofia

Bulgaria

Dubrovnik

Croatia

Split

Croatia

Zagreb

Croatia

Limassol

Cyprus

Nicosia

Cyprus

Brno

Czech Republic

Prague

Czech Republic

Aarhus

Denmark

Copenhagen

Denmark

Tallinn

Estonia

Tartu

Estonia

Helsinki

Finland

Tampere

Finland

Lyon

France

Marseille

France

Nice

France

Paris

France

Berlin

Germany

Frankfurt

Germany

Hamburg

Germany

Munich

Germany

Athens

Greece

Thessaloniki

Greece

Budapest

Hungary

Debrecen

Hungary

Akureyri

Iceland

Reykjavík

Iceland

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

St. Julian's

Malta

Valletta

Malta

Bălți

Moldova

Chișinău

Moldova

La Condamine

Monaco

Monte Carlo

Monaco

Budva

Montenegro

Podgorica

Montenegro

Amsterdam

Netherlands

Rotterdam

Netherlands

The Hague

Netherlands

Ohrid

North Macedonia

Skopje

North Macedonia

Bergen

Norway

Oslo

Norway

Gdańsk

Poland

Kraków

Poland

Warsaw

Poland

Faro

Portugal

Lisbon

Portugal

Porto

Portugal

Bucharest

Romania

Cluj-Napoca

Romania

City of San Marino

San Marino

Serravalle

San Marino

Belgrade

Serbia

Novi Sad

Serbia

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kyiv

Ukraine

Lviv

Ukraine

Odesa

Ukraine

Dubai

United Arab Emirates

Birmingham

United Kingdom

Edinburgh

United Kingdom

London

United Kingdom

Manchester

United Kingdom

Rome (Vatican-adjacent)

Vatican City

Training Outlines

Introduction

Federated Learning and Privacy Preserving AI represent the forefront of secure and ethical artificial intelligence development. This 5-day professional course is designed to equip participants with a deep understanding of federated learning principles and the methodologies used to enhance privacy in AI systems. Throughout this course, attendees will gain hands-on experience with real-world applications and learn how to integrate these compelling technologies effectively.

Understand the fundamentals of federated learning and its advantages over traditional approaches.
Gain insights into various privacy-preserving techniques employed in artificial intelligence.
Explore real-world case studies and applications of federated learning.
Learn how to implement privacy-preserving protocols in AI systems.
Develop skills to address challenges in the deployment of secure AI models.

Course Outlines

Day 1: Introduction to Federated Learning and Privacy Principles

Overview of federated learning: Concepts and significance
Comparison with centralized and decentralized learning methods
Basic privacy principles in AI and their importance
Challenges and limitations in traditional AI models concerning privacy
Introductory case studies highlighting federated learning applications

Day 2: Core Techniques in Federated Learning

Data partitioning and model aggregation methodologies
Understanding federated averaging and optimization techniques
Communication strategies between federated nodes
Overcoming challenges in data heterogeneity
Hands-on session: Setting up a basic federated learning system

Day 3: Privacy-Preserving Techniques and Algorithms

Differential privacy and its application in AI
Secure multi-party computation and homomorphic encryption
Challenges in balancing privacy, utility, and efficiency
Exploring adversarial attacks and defenses in federated settings
Practical exercises on implementing privacy algorithms

Day 4: Real-world Applications and Case Studies

Federated learning in healthcare: Opportunities and case studies
Implementation in autonomous vehicles and IoT
Federal banking and finance applications
Exploration of the use of federated learning in social media and advertising
Enterprise-level data privacy case studies

Day 5: Deployment Challenges and Future Directions

Scalability and infrastructure considerations
Legal and ethical considerations in privacy-preserving AI
Future trends and enhancements in federated learning
Strategizing the implementation of federated learning across industries
Final project: Designing a federated learning model with privacy measures

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Federated Learning and Privacy Preserving AI Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.