VA, Stafford - Quantico Corporate

800 Corporate Drive, Suite 301, Stafford, united states, 22554

The venue is located between interstate 95 and the Jefferson Davis Highway, in the vicinity of the Courtyard by Mariott Stafford Quantico and the UMUC Quantico Cororate Center.

VA, Fredericksburg - Central Park Corporate Center

1320 Central Park Blvd., Suite 200, Fredericksburg, united states, 22401

The venue is located behind a complex of commercial buildings with the Bank of America just on the corner before the turn leading to the office.

VA, Richmond - Two Paragon Place

Two Paragon Place, 6802 Paragon Place Suite 410, Richmond, United States, 23230

The venue is located in bustling Richmond with Hampton Inn, Embassy Suites and Westin Hotel less than a mile away.

VA, Reston - Sunrise Valley

12020 Sunrise Valley Dr #100, Reston, United States, 20191

The venue is located just behind the NCRA and Reston Plaza Cafe building and just next door to the United Healthcare building.

VA, Reston - Reston Town Center I

11921 Freedom Dr #550, Reston, united states, 20190

The venue is located in the Reston Town Center, near Chico's and the Artinsights Gallery of Film and Contemporary Art.

VA, Richmond - Sun Trust Center Downtown

919 E Main St, Richmond , united states, 23219

The venue is located in the Sun Trust Center on the crossing of E Main Street and S to N 10th Street just opposite of 7 Eleven.

Explore Our Courses

Federated Learning and Edge AI

 21 Hours
This instructor-led, live training in Virginia (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 Virginia (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 Virginia (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 Virginia (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 Virginia (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 Virginia (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 Virginia (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 Virginia (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

2025-05-14 9:30 am
14 hours
VA, Reston - Sunrise Valley
$ 2703 (Online)
$ 3703 (Classroom)

Federated Learning for Finance

2025-05-28 9:30 am
14 hours
VA, Reston - Reston Town Center I
$ 2703 (Online)
$ 3703 (Classroom)

Advanced Federated Learning Techniques

2025-06-11 9:30 am
21 hours
VA, Richmond - Sun Trust Center Downtown
$ 4054 (Online)
$ 5554 (Classroom)

Federated Learning in IoT and Edge Computing

2025-06-25 9:30 am
14 hours
VA, Stafford - Quantico Corporate
$ 2703 (Online)
$ 3703 (Classroom)

Advanced Federated Learning Techniques

2025-07-09 9:30 am
21 hours
VA, Fredericksburg - Central Park Corporate Center
$ 4054 (Online)
$ 5554 (Classroom)

Federated Learning for Healthcare

2025-07-23 9:30 am
21 hours
VA, Richmond - Two Paragon Place
$ 4054 (Online)
$ 5554 (Classroom)

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