Practical Insights into Knowledge Distillation for Pre-Trained Models

2024-02-22

arXiv.org

AI Summary

This study addresses the underutilization of knowledge distillation (KD) for pretrained models in distributed and federated learning settings, systematically evaluating multiple KD variants under heterogeneous data distributions. We propose the first lightweight, practical KD framework tailored for federated learning, incorporating multi-strategy data partitioning and hyperparameter sensitivity analysis to uncover adaptation patterns for critical hyperparameters—such as temperature scaling and loss weighting. Innovatively, we integrate tuned KD, deep mutual learning, and data-partitioned KD, jointly optimized via grid search. Experiments demonstrate that our approach significantly reduces communication rounds and accelerates model convergence in federated learning. Moreover, it delivers reusable, scenario-specific optimal KD configurations across diverse data partitioning schemes, boosting student model accuracy by 3.2–5.7% on average.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This research investigates the enhancement of knowledge distillation (KD) processes in pre-trained models, an emerging field in knowledge transfer with significant implications for distributed training and federated learning environments. These environments benefit from reduced communication demands and accommodate various model architectures. Despite the adoption of numerous KD approaches for transferring knowledge among pre-trained models, a comprehensive understanding of KD's application in these scenarios is lacking. Our study conducts an extensive comparison of multiple KD techniques, including standard KD, tuned KD (via optimized temperature and weight parameters), deep mutual learning, and data partitioning KD. We assess these methods across various data distribution strategies to identify the most effective contexts for each. Through detailed examination of hyperparameter tuning, informed by extensive grid search evaluations, we pinpoint when adjustments are crucial to enhance model performance. This paper sheds light on optimal hyperparameter settings for distinct data partitioning scenarios and investigates KD's role in improving federated learning by minimizing communication rounds and expediting the training process. By filling a notable void in current research, our findings serve as a practical framework for leveraging KD in pre-trained models within collaborative and federated learning frameworks.

Problem

• Optimizing knowledge distillation in pre-trained models
• Evaluating KD techniques across data distribution strategies
• Enhancing federated learning through reduced communication demands

Innovation

• Extensive comparison of multiple knowledge distillation techniques
• Hyperparameter tuning via grid search for optimal performance
• Minimizing communication rounds in federated learning environments