The proliferation of connected objects in the Internet of Things (IoT) ecosystem presents challenges in enabling real-time intelligence while safeguarding data privacy, especially within the computational and energy limitations of edge devices. This study, based on simulation and synthetic data collection methods, addresses these challenges by proposing a lightweight edge AI framework that employs federated learning, enabling model training across distributed Internet of People and Things (IoP) devices without transferring raw data to centralised servers. The framework integrates on-device inference, stochastic local updates, and model compression to ensure low-latency decision-making while adhering to memory and energy constraints. To enhance security, differential privacy mechanisms, encrypted aggregation, and robust outlier detection are utilized to defend against adversarial and Byzantine attacks. The proposed framework offers an effective solution for deploying federated intelligence on resource-constrained IoP devices, enabling responsive, privacy-preserving, and resilient edge AI operations in distributed environments.