Federated Learning Economics: Privacy vs Efficiency

Key Points

• This research analyzes the economic implications of federated learning in AI projects, focusing on cost and compliance aspects.
• Economic analysis of federated learning implementations
• Examination of computational overhead and communication costs
• Assessment of regulatory compliance values and decision frameworks
• Identified key cost factors in federated versus centralized approaches
• Demonstrated that federated learning can offer privacy benefits despite higher computational costs
• Provided frameworks for decision-making regarding AI project strategies