Multi-modal human activity recognition (HAR) is a key technology for a wide range of applications and has received widespread attention in recent years. However, the difficulty of achieving generalizability in multi-modal sensing models, combined with heterogeneous and unlabeled downstream data, significantly hinders their broader adoption. In this work, we proposePFHAR, a unified framework for practically adopting multi-modal foundation HAR model to target user groups.PFHARuses a novel dynamic masked contrastive learning method to pre-train a foundation model on various heterogeneous public HAR datasets, ensuring strong generalizability across different modal combinations. It then adopts semi-supervised edge-cloud collaborative learning to fine-tune the pre-trained model with heterogeneous and unlabeled local data, adapting it for the target user group. Our evaluations on public and self-collected datasets demonstrate thatPFHARsignificantly outperforms SOTA baselines in both the pre-training and edge-cloud collaborative fine-tuning stages.