Privacy preserving federated learning for full heterogeneity

Area of Science:

• Machine Learning
• Distributed Systems
• Data Science

Background:

• Federated learning enables collaborative model training while preserving data privacy.
• Practical federated learning faces challenges like data, model, and computation heterogeneity, degrading performance.
• Existing solutions inadequately address multiple heterogeneity challenges simultaneously.

Purpose of the Study:

• To introduce Full Heterogeneous Federated Learning (FHFL), a novel approach.
• To simultaneously address data, model, and computation heterogeneity in federated learning.
• To improve global model performance in diverse federated learning environments.

Main Methods:

• Synthetic data generation to mitigate Non-IID data heterogeneity.
• Knowledge distillation for aggregating insights from heterogeneous client models.
• Opportunistic computation scheduling to leverage idle resources for faster clients.

Main Results:

• FHFL demonstrates excellent model training performance across various datasets.
• The proposed methods effectively address multiple heterogeneity challenges.
• Significant improvements in global model performance were observed.

Conclusions:

• FHFL offers a pioneering solution for distributed model training among heterogeneous clients.
• The approach effectively tackles the complex interplay of heterogeneity in federated learning.
• FHFL paves the way for more robust and efficient federated learning systems.