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Te Pou Toetoe Linwood

141 Smith Street, Linwood, Christchurch, New Zealand

Te Pou Toetoe Linwood Pool is a well-equipped, accessible venue ideal for hosting training courses, workshops, and education places the facility provides a convenient and professional environment for both instructors and participants. The venue offers easy access for attendees, with on-site parking available directly outside the main entrance, ensuring smooth arrival for course groups and equipment transport. For those travelling by public transport, the facility is serviced by multiple bus routes, including 80, 155, and the Orbiter, with nearby stops providing straightforward connections from across Christchurch. With its modern amenities, accessible layout, and convenient transport links, Te Pou Toetoe Linwood Pool is an excellent choice for delivering practical, engaging, and well-supported training sessions.

Explore Our Courses

Federated Learning and Edge AI

 21 Hours

This instructor-led, live training in Christchurch (online or on-site) is designed for advanced-level AI researchers, data scientists, and security specialists who wish to implement federated learning techniques for training 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.
  • Optimise 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.
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Federated Learning for Secure AI Collaboration

 14 Hours

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

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

  • Understand the core principles and benefits of Federated Learning in IoT and edge computing contexts.
  • Implement Federated Learning models on IoT devices to enable decentralised AI processing.
  • Reduce latency and enhance real-time decision-making in edge computing environments.
  • Tackle challenges related to data privacy and network constraints in IoT systems.
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Federated Learning for Finance

 14 Hours

This instructor-led, live training in Christchurch (online or on-site) is aimed at 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.
  • Analyse financial data collaboratively without compromising privacy.
  • 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 Christchurch (delivered online or on-site) is designed for intermediate-level professionals seeking 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 organisations.
  • 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 Christchurch (online or onsite) is designed for advanced-level professionals seeking 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 for enhanced performance.
  • Manage non-IID data distributions within Federated Learning frameworks.
  • Scale Federated Learning systems for large-scale 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 Christchurch (online or onsite) is aimed at intermediate-level professionals who wish 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 decentralised AI training.
  • Apply Federated Learning in real-world scenarios across various industries.
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Introduction to Federated Learning

 14 Hours

This instructor-led, live training in Christchurch (online or on-site) is aimed at beginner-level professionals who wish to learn 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.
Read more...

Last Updated:

Provisional Upcoming Courses (Require 5+ participants)

Federated Learning and Edge AI

2026-06-22 09:30
21 hours
Te Pou Toetoe Linwood
8160 NZD (Online)
8160 NZD (Classroom)

Advanced Federated Learning Techniques

2026-07-06 09:30
21 hours
Te Pou Toetoe Linwood
8160 NZD (Online)
8160 NZD (Classroom)

Advanced Federated Learning Techniques

2026-07-20 09:30
21 hours
Te Pou Toetoe Linwood
8160 NZD (Online)
8160 NZD (Classroom)

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