Austin, TX – Regus at Dobie Center

2021 Guadalupe Street, Suite 260, Austin, United States, 78705

The venue is located in the vibrant West Campus neighborhood near the University of Texas at the Dobie Center, easily reachable by car via I‑35 or I‑35 Frontage Road and Loop 1, with on-site and street parking nearby. From Austin–Bergstrom International Airport (AUS), about 11 miles southeast, a taxi or rideshare takes around 20–25 minutes via TX‑71 West and I‑35 North. Public transit users can take Capital Metro routes to the University of Texas/Dobie Mall stop directly in front of the building, placing it within a short walk for attendees without cars.

Austin, TX - Regus - Littlefield Congress

106 E 6th St, Austin, United States, 78701

The venue is conveniently situated near I‑35 and US‑183, making it easily accessible by car, with both public and garage parking options—such as the Littlefield Garage at 508 Brazos—located within walking distance. For those using public transportation, the venue is just a short walk from the Downtown Station (CapMetro) and is served by several bus routes. Travelers arriving by air will find that Austin-Bergstrom International Airport is approximately 8 miles away, providing straightforward access to and from the venue.

Explore Our Courses

Federated Learning and Edge AI

 21 Hours

This instructor-led, live training in Austin (online or onsite) is aimed at 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.
  • 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.
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Federated Learning for Secure AI Collaboration

 14 Hours

This instructor-led, live training in Austin (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 Austin (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

  • Understand the principles and benefits of Federated Learning in IoT and edge computing.
  • Implement Federated Learning models on IoT devices for decentralized AI processing.
  • Reduce latency and improve real-time decision-making in edge computing environments.
  • Address 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 Austin (online or onsite) 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.
  • Analyze 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 Austin (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.
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Advanced Federated Learning Techniques

 21 Hours

This instructor-led, live training in Austin (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.
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Federated Learning for Privacy-Preserving AI

 14 Hours

This instructor-led, live training in Austin (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 decentralized 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 Austin (online or onsite) 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.
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Upcoming Courses

Federated Learning for Finance

2025-08-13 9:30 am
14 hours
Austin, TX - Regus - Littlefield Congress
$ 2703 (Online)
$ 2703 (Classroom)

Federated Learning for Finance

2025-08-27 9:30 am
14 hours
Austin, TX – Regus at Dobie Center
$ 2703 (Online)
$ 2703 (Classroom)

Advanced Federated Learning Techniques

2025-09-10 9:30 am
21 hours
Austin, TX - Regus - Littlefield Congress
$ 4054 (Online)
$ 4054 (Classroom)

Federated Learning in IoT and Edge Computing

2025-09-24 9:30 am
14 hours
Austin, TX – Regus at Dobie Center
$ 2703 (Online)
$ 2703 (Classroom)

Advanced Federated Learning Techniques

2025-10-08 9:30 am
21 hours
Austin, TX - Regus - Littlefield Congress
$ 4054 (Online)
$ 4054 (Classroom)

Federated Learning for Healthcare

2025-10-22 9:30 am
21 hours
Austin, TX – Regus at Dobie Center
$ 4054 (Online)
$ 4054 (Classroom)

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