Federated Learning and Privacy Preserving AI Course

Master federated learning, distributed optimisation, secure aggregation, differential privacy and governed collaborative AI deployment.

Artificial Intelligence and Machine Learning Courses Confirmed

Federated Learning and Privacy Preserving AI Course

Training Outlines

Introduction

Federated learning enables multiple devices or organisations to train shared models without centralising their raw data. It can support collaboration across sensitive or distributed environments, but decentralised training is not automatically private or secure. Gradients may leak information, participants may poison updates and unequal data distributions can reduce performance or fairness.

This five-day course develops practical competence in federated learning and privacy-preserving AI. Participants will design federated architectures, implement aggregation and optimisation, apply privacy and security controls, manage heterogeneous clients and evaluate system-level performance. The course combines technical implementation with governance for cross-device and cross-organisation collaboration.

Objectives

By the end of this course, participants will be able to:

Select suitable federated-learning use cases.
Design cross-device and cross-silo architectures.
Implement federated averaging and optimisation.
Manage non-identical client data.
Apply secure aggregation and differential privacy.
Recognise leakage, poisoning and backdoor threats.
Evaluate utility, fairness and communication cost.
Design a governed federated-AI deployment.

Course Content

Day 1: Federated Architecture and Threat Models

Centralised versus federated learning
Cross-device and cross-silo settings
Horizontal and vertical federation
Participants, trust and data boundaries
Privacy and security threat models
Federated-use-case design workshop

Day 2: Federated Training and Optimisation

Local training and communication rounds
Federated averaging
Client sampling and update weighting
Federated optimisation methods
Communication-efficient training
Federated-training laboratory

Day 3: Privacy and Security Controls

Gradient and update leakage
Secure aggregation
Differentially private training
Homomorphic-encryption concepts
Poisoning and backdoor attacks
Privacy-control laboratory

Day 4: Heterogeneity and Evaluation

Non-IID client distributions
Personalisation and local adaptation
Client dropout and stragglers
Convergence and stability assessment
Client-level utility and fairness
Federated-system evaluation exercise

Day 5: Production and Collaborative Governance

Federated orchestration and identity
Model updates and version control
Performance and drift monitoring
Consent, ownership and data residency
Incident response across participants
Capstone federated-AI architecture

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

Jakarta

Indonesia

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Osaka

Japan

Tokyo

Japan

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

Kuala Lumpur

Malaysia

Malé

Maldives

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

Abuja

Nigeria

Lagos

Nigeria

Port Harcourt

Nigeria

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

Singapore

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Incheon

South Korea

Seoul

South Korea

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Sri Jayawardenepura Kotte

Sri Lanka

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kaohsiung

Taiwan

Taipei

Taiwan

Ankara

Turkey

Istanbul

Turkey

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 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.