## 1 Introduction
The similarity in the properties between HRV and PRV is higher when PPG signals are not excessively affected by motion artifacts and noise. In the temporal domain, AF irregularities follow similar statistical patterns in both modalities because they originate from the same arrhythmic cardiac activity. In the frequency domain, the chaotic frequency patterns characteristic of AF are preserved across both signals. Here, transfer learning (TL) is a paradigm that is leveraged to adapt knowledge mined on a specific task to another closely related secondary task. While absolute values may differ between HRV and PRV due to physiological transit time and vascular compliance factors, the relative patterns and feature relationships critical for AF classification remain consistent, making learned feature hierarchies transferable. Some of the limitations we face when developing PPG-based analytical models that make TL appealing include the scarcity of publicly available, universally reviewed benchmark databases and the degradation of model performance due to low signal quality [23]. There are also many influencing factors such as skin tone, motion artifacts and signal crossover, body temperature, and ambient light, as well as sensor design characteristics and positioning [24]. The process of manually annotating datasets is complex and suffers from inter-rater variability, which occurs when multiple experts working on a dataset assign different labels to the same data instance [25]. Reaching a consensus among experts is difficult if the data are not optimally preprocessed, which is an issue present in the majority of PPG signal annotation efforts in the literature. Furthermore, most of the previously developed algorithms are only applicable in controlled clinical settings, making early prognosis inaccessible to the general population. Another limitation in the usage of large deep learning models with complex architectures for medical screening is their high requirements for memory, computational power, and storage space, making them inappropriate for deployment in real-world applications. Knowledge distillation (KD) is a method that compresses a complex model into a smaller and faster model without significant performance loss [26]. This method employs a larger network (called the _teacher_) to train a smaller network (called the _student_) to help the latter with improved generalization by virtue of the knowledge gained by the complex _teacher_ model. This knowledge is transferred to the simplified _student_ model and used for supervision of its training. KD can be used to introduce complementary information from additional modalities during training while avoiding their computation during testing [27]. Research has shown that the usage of KD results in greater generalization capabilities and faster training [28]. KD has previously been used to create small and robust models for tasks such as arrhythmia detection, sleep staging, and emotion recognition using ECG signals [28; 29; 30; 31; 32; 33]. Essentially, within the scope of our experiments, the _teacher_ model is trained on an ECG dataset, while the _student_ model is the smaller variant that is fine-tuned with the PPG dataset and trained using the soft labels generated by the _teacher_. Hence, in accordance with the literature, we will be using the synonymous terms _teacher_ and _student_ to refer to these models throughout this work. This work focuses on classifying normal sinus rhythm (NSR) and AF. NSR represents the rhythm of the healthy human heart, which originates from the sinus node [34]. In order to address the lack of large-scale annotated PPG datasets, our work proposes the usage of a deep learning model trained on a large ECG dataset to assist in enhancing the PPG-based model, as ECG datasets are relatively better annotated and compiled [35].