Affective Computing (AfC) aims to detect, interpret, and respond to human emotions by analyzing multimodal psychophysiological signals, including facial expressions, vocal cues, and physiological data such as ECG, EEG, and EDA. To improve system responsiveness and privacy, Affective Edge Computing (AEC) integrates AfC with edge computing frameworks, enabling real-time, localized processing of emotion data on devices such as wearables, smartphones, and embedded systems. This approach reduces latency, minimizes network dependency, and preserves user privacy by retaining sensitive data at the source. Lightweight machine learning and signal processing models on edge devices enable efficient, real-time emotion classification within limited computational resources. However, developing a scalable, distributed architecture that integrates multimodal data acquisition, local emotion recognition, optional fog-based load balancing, and cloud-based analytics remains a key challenge. Ensuring responsiveness and efficiency is especially critical in sensitive applications such as mental health monitoring, human-robot interaction, and mobile affective systems, where privacy, low latency, and reliability are essential.