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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 -
- Bespoke outlines
- 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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Public Training
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