Choose from online or onsite, instructor-led live Federated Learning training courses that demonstrate, through interactive hands-on practice, how to leverage decentralized machine learning techniques. These courses focus on training models across distributed data sources while ensuring that sensitive data remains private and is not shared.
Federated Learning training is offered in two formats: "online live training" or "onsite live training". The online live training (also referred to as "remote live training") is delivered via an interactive remote desktop session. Alternatively, onsite live training can be conducted locally on customer premises in Prague or at NobleProg corporate training centers located in Prague.
Please note that Federated Learning is also commonly known as Collaborative Learning.
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This instructor-led, live training in Prague (online or onsite) is designed for advanced AI researchers, data scientists, and security specialists who wish to implement federated learning techniques to train AI models across multiple edge devices while preserving data privacy.
By the end of this training, participants will be able to:
Understand the principles and benefits of federated learning in Edge AI.
Implement federated learning models using TensorFlow Federated and PyTorch.
Optimize AI training across distributed edge devices.
Address data privacy and security challenges in federated learning.
Deploy and monitor federated learning systems in real-world applications.
This instructor-led live training, located in Prague (available online or onsite), is intended for intermediate-level AI and data professionals who want to understand and apply federated learning techniques for privacy-preserving machine learning and collaborative AI solutions across distributed data sources.
Upon completion of this training, participants will be capable of:
Grasping the fundamental concepts and advantages of federated learning.
Executing distributed training strategies for AI models.
Applying federated learning methods to secure collaborations involving sensitive data.
Examining case studies and practical examples of federated learning applications in healthcare and finance.
This instructor-led, live training in Prague (available online or onsite) is designed for intermediate-level professionals aiming to apply Federated Learning to optimize IoT and edge computing solutions.
Upon completion of this training, participants will be able to:
Grasp the fundamental principles and advantages of Federated Learning in IoT and edge computing contexts.
Deploy Federated Learning models on IoT devices to facilitate decentralized AI processing.
Minimize latency and enhance real-time decision-making capabilities in edge computing environments.
Overcome challenges associated with data privacy and network constraints in IoT systems.
This instructor-led, live training in Prague (online or onsite) targets intermediate-level professionals who wish to apply Federated Learning techniques to enhance data privacy and collaborative AI in the financial industry.
By the end of this training, participants will be able to:
Understand the principles and benefits of Federated Learning in finance.
Implement Federated Learning models for privacy-preserving financial applications.
Analyze financial data collaboratively without compromising privacy.
Apply Federated Learning to real-world financial scenarios, such as fraud detection and risk management.
This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to healthcare scenarios, ensuring data privacy and effective collaboration across institutions.
By the end of this training, participants will be able to:
Understand the role of Federated Learning in healthcare.
Implement Federated Learning models while ensuring patient data privacy.
Collaborate on AI model training across multiple healthcare institutions.
Apply Federated Learning to real-world healthcare case studies.
This instructor-led, live training in Prague (online or onsite) is aimed at advanced-level professionals who wish 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:
Optimize Federated Learning algorithms for improved performance.
Handle non-IID data distributions in Federated Learning.
Scale Federated Learning systems for large-scale deployments.
Address privacy, security, and ethical considerations in advanced Federated Learning scenarios.
This instructor-led, live training in Prague (online or onsite) is designed for intermediate-level professionals who want to understand and apply Federated Learning to ensure data privacy in AI development.
By the end of this training, participants will be able to:
Understand the principles and benefits of Federated Learning.
Implement privacy-preserving machine learning models using Federated Learning techniques.
Address the challenges of data privacy in decentralized AI training.
Apply Federated Learning in real-world scenarios across various industries.
This live, instructor-led training in Prague (available online or in-person) is designed for beginner-level professionals aiming to master the fundamentals of Federated Learning and its practical applications.
By the end 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.
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