Towards privacy-preserving and communication-efficient federated distillation

Key Points

• Federated distillation enhances communication efficiency while preserving privacy across distributed nodes.
• The method improves data aggregation by reducing bandwidth usage by 30% in comparative analysis.
• Analysis of federated learning frameworks demonstrates advancements in privacy-preserving techniques.
• Future work may clarify scalability and effectiveness in diverse real-world applications.