  The personalization of machine learning (ML) models to address data drift is
a significant challenge in the context of Internet of Things (IoT)
applications. Presently, most approaches focus on fine-tuning either the full
base model or its last few layers to adapt to new data, while often neglecting
energy costs. However, various types of data drift exist, and fine-tuning the
full base model or the last few layers may not result in optimal performance in
certain scenarios. We propose Target Block Fine-Tuning (TBFT), a low-energy
adaptive personalization framework designed for resource-constrained devices.
We categorize data drift and personalization into three types: input-level,
feature-level, and output-level. For each type, we fine-tune different blocks
of the model to achieve optimal performance with reduced energy costs.
Specifically, input-, feature-, and output-level correspond to fine-tuning the
front, middle, and rear blocks of the model. We evaluate TBFT on a ResNet
model, three datasets, three different training sizes, and a Raspberry Pi.
Compared with the $Block Avg$, 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.
