Fault diagnosis in distributed device clusters (DDCs) is essential for ensuring safe and reliable operation of modern automated systems. However, practical deployments face two fundamental challenges: severe class imbalance from scarce device-level fault data and strict resource constraints on edge nodes. This paper proposes a Lightweight Diffusion-Enhanced Multi-Domain Fusion (LDEMF) framework that addresses both challenges through integrated data balancing, representation learning, and efficient deployment. A dual-attention conditional diffusion model synthesizes class-balanced vibration samples preserving temporal, spectral, and non-stationary fault characteristics. A multi-domain feature fusion network then integrates time-, frequency-, and time-frequency-domain representations via cross-attention for robust fault characterization. Finally, structured channel pruning, knowledge distillation, and FP16 post-training quantization enable resource-efficient edge deployment. Experimental results on the CWRU bearing dataset and a self-collected industrial robot dataset demonstrate that LDEMF achieves superior diagnostic accuracy under moderate-to-severe class imbalance. The framework reduces model size, computational cost, and inference latency by up to an order of magnitude with negligible performance loss, validating its effectiveness for edge-level fault diagnosis in DDCs.