Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task...

Area of Science:

• Machine Learning
• Internet of Things (IoT)
• Data Privacy

Background:

• IoT device proliferation necessitates machine learning integration.
• Federated learning addresses data privacy concerns but faces challenges like heterogeneity and communication costs.
• Existing methods struggle with Non-IID (Non-Independent and Identically Distributed) IoT data.

Purpose of the Study:

• To propose a personalized joint learning algorithm for Non-IID IoT data within federated learning.
• To address data and device heterogeneity in federated learning environments.
• To enhance privacy preservation and model accuracy in IoT machine learning.

Main Methods:

• Developed a personalized joint learning algorithm incorporating multi-task learning and neural network characteristics.
• Introduced a novel automatic clustering algorithm for federated learning, eliminating the need for pre-defined cluster counts.
• Conducted extensive experiments to evaluate algorithm performance.

Main Results:

• The proposed algorithm demonstrates exceptional performance, especially with specific client distributions.
• Significant improvements in the accuracy of trained models were observed.
• The approach effectively addresses data heterogeneity and strengthens privacy preservation.

Conclusions:

• The study offers a robust solution for federated learning challenges in IoT.
• The combination of personalized learning and automatic clustering enhances privacy-conscious machine learning for Non-IID IoT data.
• This work facilitates more effective and secure machine learning applications in IoT ecosystems.