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Mirobod

21A, Taras Shevchenko Street, Mirabad, Mirobod, Uzbekistan, 100015

The Centre is located at Taras Shevchenko Street, in the central Mirabad district of Tashkent, Uzbekistan. The location is conveniently placed near the Ming Urik metro station and is also close to the Oybek metro interchange, making it easily accessible by public transport from different parts of the city. The Centre is approximately 5–6 kilometres from Islam Karimov Tashkent International Airport, depending on the route and traffic conditions. The area is well-connected and sits close to key central Tashkent landmarks, business areas, and hospitality options. Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate office.

Explore Our Courses

Federated Learning and Edge AI

 21 Hours

This instructor-led, live training in Tashkent (available online or on-site) is designed for advanced-level AI researchers, data scientists, and security specialists who aim to implement federated learning techniques for training AI models across multiple edge devices while maintaining data privacy.

By the conclusion of this training, participants will be able to:

  • Comprehend the principles and advantages of federated learning within Edge AI.
  • Implement federated learning models using TensorFlow Federated and PyTorch.
  • Optimise AI training across distributed edge devices.
  • Tackle data privacy and security challenges inherent in federated learning.
  • Deploy and monitor federated learning systems in real-world applications.
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Federated Learning for Secure AI Collaboration

 14 Hours

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level AI and data professionals who wish to understand and implement federated learning techniques for privacy-preserving machine learning and collaborative AI solutions across distributed data sources.

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

  • Understand the core concepts and benefits of federated learning.
  • Implement distributed training strategies for AI models.
  • Apply federated learning techniques to secure data-sensitive collaborations.
  • Explore case studies and practical examples of federated learning in healthcare and finance.
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Federated Learning in IoT and Edge Computing

 14 Hours

This instructor-led, live training in Tashkent (available online or on-site) is designed for intermediate-level professionals seeking to leverage Federated Learning to optimize IoT and edge computing solutions.

By the conclusion of this training, participants will be able to:

  • Grasp the core principles and advantages of Federated Learning in IoT and edge computing.
  • Deploy Federated Learning models on IoT devices to enable decentralized AI processing.
  • Minimize latency and enhance real-time decision-making capabilities in edge computing environments.
  • Tackle challenges related to data privacy and network constraints within IoT systems.
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Federated Learning for Finance

 14 Hours

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level professionals who wish to apply Federated Learning techniques to strengthen data privacy and foster collaborative AI within the financial industry.

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

  • Understand the core principles and benefits of Federated Learning in finance.
  • Implement Federated Learning models for privacy-preserving financial applications.
  • Collaboratively analyse financial data without compromising confidentiality.
  • Apply Federated Learning to real-world financial scenarios, such as fraud detection and risk management.
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Federated Learning for Healthcare

 21 Hours

This instructor-led, live training in Tashkent (available online or onsite) is designed for intermediate-level professionals seeking to apply Federated Learning to healthcare scenarios, ensuring data privacy and effective inter-institutional collaboration.

Upon completion of this training, participants will be able to:

  • Understand the role of Federated Learning in healthcare.
  • Implement Federated Learning models while maintaining patient data privacy.
  • Collaborate on AI model training across multiple healthcare institutions.
  • Apply Federated Learning to real-world healthcare case studies.
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Advanced Federated Learning Techniques

 21 Hours

This instructor-led, live training in Tashkent (online or on-site) is designed for advanced-level professionals aiming to master cutting-edge Federated Learning techniques and apply them to large-scale AI projects.

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

  • Optimise Federated Learning algorithms to achieve enhanced performance.
  • Manage non-IID data distributions within Federated Learning frameworks.
  • Scale Federated Learning systems for extensive deployments.
  • Address privacy, security, and ethical considerations in advanced Federated Learning scenarios.
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Federated Learning for Privacy-Preserving AI

 14 Hours

This instructor-led, live training in Tashkent (available online or on-site) is designed for intermediate-level professionals who wish to understand and apply Federated Learning to safeguard data privacy in AI development.

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

  • Grasp the core principles and advantages of Federated Learning.
  • Develop privacy-preserving machine learning models using Federated Learning techniques.
  • Overcome data privacy challenges in decentralized AI training environments.
  • Apply Federated Learning to real-world scenarios across diverse industries.
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Introduction to Federated Learning

 14 Hours

This instructor-led, live training in Tashkent (online or onsite) targets beginner-level professionals aiming to learn the fundamentals of Federated Learning and its practical applications.

By the conclusion of this training, participants will be able to:

  • Understand the principles of Federated Learning.
  • Implement basic Federated Learning algorithms.
  • Address data privacy concerns using Federated Learning.
  • Integrate Federated Learning into existing AI workflows.
Read more...

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Upcoming Courses

Advanced Federated Learning Techniques

2026-06-26 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

Advanced Federated Learning Techniques

2026-07-10 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

Federated Learning and Edge AI

2026-07-24 09:30
21 hours
Mirobod
33,896,260 UZS (Online)
36,896,260 UZS (Classroom)

Federated Learning for Secure AI Collaboration

2026-08-07 09:30
14 hours
Mirobod
22,597,506 UZS (Online)
24,597,506 UZS (Classroom)

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