Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation

TL;DR

This paper introduces a novel federated learning algorithm that combines model delta regularization, personalized models, federated knowledge distillation, and mix-pooling to effectively handle client heterogeneity, improve accuracy, and accelerate convergence.

Contribution

It presents a new FL optimization approach integrating multiple techniques to address data and task heterogeneity, with demonstrated superior performance.

Findings

Model delta regularization enhances accuracy and convergence speed.

Federated knowledge distillation improves performance with diverse data.

Mix-pooling benefits clients by accommodating readout variations.

Abstract

Federated learning (FL) has emerged as a transformative training paradigm, particularly invaluable in privacy-sensitive domains like healthcare. However, client heterogeneity in data, computing power, and tasks poses a significant challenge. To address such a challenge, we propose an FL optimization algorithm that integrates model delta regularization, personalized models, federated knowledge distillation, and mix-pooling. Model delta regularization optimizes model updates centrally on the server, efficiently updating clients with minimal communication costs. Personalized models and federated knowledge distillation strategies are employed to tackle task heterogeneity effectively. Additionally, mix-pooling is introduced to accommodate variations in the sensitivity of readout operations. Experimental results demonstrate the remarkable accuracy and rapid convergence achieved by model delta regularization. Additionally, the federated knowledge distillation algorithm notably improves FL performance, especially in scenarios with diverse data. Moreover, mix-pooling readout operations provide tangible benefits for clients, showing the effectiveness of our proposed methods.