Course Outline

Introduction to Federated Learning in Finance

  • Overview of Federated Learning concepts and benefits
  • Challenges in implementing Federated Learning in finance
  • Use cases of Federated Learning in the financial industry

Privacy-Preserving AI Techniques

  • Ensuring data privacy in Federated Learning models
  • Techniques for secure data aggregation and analysis
  • Compliance with financial data privacy regulations

Federated Learning Applications in Finance

  • Fraud detection using Federated Learning
  • Risk management and predictive analytics
  • Collaborative AI for regulatory compliance

Implementing Federated Learning in Financial Systems

  • Setting up Federated Learning environments
  • Integrating Federated Learning into existing financial workflows
  • Case studies of successful implementations

Future Trends in Federated Learning for Finance

  • Emerging technologies and methodologies
  • Scalability and performance optimization
  • Exploring future directions in Federated Learning

Summary and Next Steps

Requirements

  • Experience in finance or financial data analysis
  • Basic understanding of AI and machine learning
  • Familiarity with data privacy regulations

Audience

  • Financial data scientists
  • AI developers in finance
  • Data privacy officers in the financial sector
 14 Hours

Delivery Options

Private Group Training

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  • Pre-course call with your trainer
  • Customisation of the learning experience to achieve your goals -
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    • Practical hands-on exercises containing data / scenarios recognisable to the learners
  • Training scheduled on a date of your choice
  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €4560 online delivery, based on a group of 2 delegates, €1440 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.

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