Low-Energy Adaptive Personalization for Resource-Constrained Devices

Core Concepts

Target Block Fine-Tuning (TBFT) optimizes model performance with reduced energy costs by fine-tuning specific blocks based on data drift types.

Abstract

• Introduction:
  • Challenges of ML on IoT devices.
  • Approaches to address personalization issues.
• Background:
  • Transfer learning and its benefits.
  • Importance of model robustness to domain shifts.
• Adaptive Personalization:
  • Assumptions and categorization of data drift types.
  • Implementation of TBFT framework.
• Implementation & Evaluation Setup:
  • ResNet-26 model used for experiments.
  • Datasets employed for different drift types.
• Preliminary Evaluation Results:
  • Motivation experiments showcasing the effectiveness of TBFT.
  • Model performance results for different drift types and training sizes.
• System Cost:
  • Energy savings achieved by TBFT compared to full model fine-tuning.

Stats

Compared with the 𝐵𝑙𝑜𝑐𝑘𝐴𝑣𝑔, where each block is fine-tuned individually and their performance improvements are averaged, TBFT exhibits an improvement in model accuracy by an average of 15.30% whilst saving 41.57% energy consumption on average compared with full fine-tuning.

Quotes

Key Insights Distilled From

Towards Low-Energy Adaptive Personalization for Resource-Constrained Devices

by Yushan Huang... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15905.pdf

Deeper Inquiries

How can TBFT be extended to handle more complex scenarios involving multiple types of data drift?

To extend TBFT for handling more complex scenarios with multiple types of data drift, a multidimensional approach can be adopted. This involves detecting and ranking the importance of different types of drift using techniques like Discrimination Head. By incorporating unsupervised personalization methods, such as MEMO or clustering-based classifiers, TBFT can adapt to various drifts without prior knowledge. Additionally, a segmented and joint optimization strategy could be implemented to gradually adjust to different types of data drift based on their characteristics.

What are the implications of unsupervised personalization methods in the context of resource-constrained devices?

Unsupervised personalization methods offer significant advantages in resource-constrained environments by reducing the need for labeled target data. Techniques like MEMO focus on minimizing marginal entropy for individual images, enabling adaptation without explicit supervision. However, these methods may not always align with true label distributions in output-level shifts. Clustering-based classifiers provide an alternative solution that relies on feature representations rather than labels, making them effective even in cases where label information is unreliable due to distribution shifts.

How can discrimination head techniques be leveraged to enhance the adaptability and efficiency of TBFT?

Discrimination head techniques can play a crucial role in enhancing the adaptability and efficiency of TBFT by aiding in parameter/block selection based on dataset characteristics rather than embedding it into training processes. By utilizing Discrimination Head before model training, optimal blocks for fine-tuning specific types of data drift can be identified efficiently. This proactive approach ensures that only relevant blocks are adjusted during training, reducing computational burden and improving overall performance while maintaining energy efficiency on resource-constrained devices.