Des Moines, IA - Hub Tower

699 Walnut Street, Des Moines, United States, 50309

The Hub Tower location is easily accessible by car via Interstates 35 and 80, with underground and nearby public parking available; simply head downtown to Walnut Street. Guests arriving from Des Moines International Airport (DSM) can expect a 10‑ to 15‑minute taxi or rideshare journey via I‑235 and I‑35 into downtown. For public transportation users, DART buses stop close to the building, and it also connects via the downtown skywalk to neighboring business and cultural venues.

IA, Johnston - Regus - Foxboro Square

6165 Northwest 86th Street, Johnston, United States, 50131

Regus Foxboro Square enjoys a highly accessible suburban location—and is easily reached by car via Interstates 80/35. Ample free parking is available in the surface lots surrounding the building. For those arriving from Des Moines International Airport (DSM), the venue is approximately 16.6 km (about a 15–20 minute drive) via I‑35 north. Public transit users can take Johnston-area bus services to nearby stops; the facility is also adjacent to Dover Park’s walking and biking trails, providing a healthy commute option.
NobleProg Training Place, City Cedar Rapids, IA - Regus – Edgewood Pointe

Cedar Rapids, IA - Regus – Edgewood Pointe

4515 N River Blvd NE #200, Cedar Rapids, United States, 52411

Regus Cedar Rapids is easily accessible by car via I‑380 or I‑80 toward northeast Cedar Rapids, exiting at Edgewood Road and traveling south on River Boulevard. Complimentary on-site surface parking ensures convenience for event attendees. For travelers arriving at Des Moines International Airport (DSM), the venue is approximately a 90‑minute drive via I‑80 and I‑380. Local participants can reach the centre using Cedar Rapids Transit Route 10, with stops near River Boulevard and Edgewood Pointe.

Explore Our Courses

Federated Learning and Edge AI

 21 Hours

This instructor-led, live training in Iowa (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 Iowa (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 Iowa (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 Iowa (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 Iowa (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 Iowa (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 Iowa (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 Iowa (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.
Read more...

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Upcoming Courses

Federated Learning for Finance

2025-08-13 9:30 am
14 hours
IA, Johnston - Regus - Foxboro Square
$ 2703 (Online)
$ 2703 (Classroom)

Federated Learning for Finance

2025-08-27 9:30 am
14 hours
IA, Johnston - Regus - Foxboro Square
$ 2703 (Online)
$ 2703 (Classroom)

Advanced Federated Learning Techniques

2025-09-10 9:30 am
21 hours
IA, Johnston - Regus - Foxboro Square
$ 4054 (Online)
$ 4054 (Classroom)

Federated Learning in IoT and Edge Computing

2025-09-24 9:30 am
14 hours
IA, Johnston - Regus - Foxboro Square
$ 2703 (Online)
$ 2703 (Classroom)

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