Recent breakthroughs in Transformer-based large models, have driven widespread tasks, yet their reliance on centralized cloud deployment raises significant privacy risks due to sensitive data exposure. While edge-based collaborative inference offers a privacy-preserving alternative, existing methods face critical limitations: static model partitioning cannot adapt to dynamic edge resource fluctuations, and rigid multi-head attention handling overlooks semantic-critical prioritization and parallelism. We propose EdgeFormer, a latency-aware framework for distributed Transformer inference in resource-constrained edge networks. EdgeFormer dynamically allocates model blocks across devices via efficiency-storage trade-off optimization and introduces collaborative Multi-Head Attention (cMHA), which distributes semantic-critical attention heads across devices while pruning redundant ones under real-time constraints. We further develop LiScore, a composite metric integrating attention diversity and latency costs, alongside a similarity-based retrieval method to reduce recomputation overhead. Extensive experiments demonstrate that EdgeFormer achieves up to 2.01 \\times inference acceleration over state-of-the-art baselines with \\leq1.06% accuracy loss, maintaining robustness under varying edge conditions.

Multimodal knowledge graph completion (MKGC) aims to predict missing triples in MKGs using multimodal information. Recent research typically either extracts information from each modality separately to predict, then ensembles the predictions at the decision stage, or projects multiple modalities into a unified feature space to learn multimodal representations for prediction. However, these methods usually overlook the intrinsic correlation between modalities in MKGs which should be leveraged in both unimodal knowledge extraction and multimodal knowledge fusion. Motivated by this, we propose a noval Modal-collaborative knowledge learning (Moodle) framework for MKGC, the key idea of which is to foster mutual guidance and collaboration during unimodal knowledge extraction, to let each modality acquire distinct and complementary knowledge that subsequently enhances the multimodal knowledge fusion. Specifically, Moodle preserves the representations of different modalities to learn unimodal knowledge while modeling the mutual guidance through multi-task learning. Furthermore, Moodle performs multimodal knowledge fusion and prediction guided by unimodal knowledge, capturing their synergistic relationships and acquire fine-grained semantic knowledge through contrastive learning. Extensive experiments on three real-world datasets demonstrate the advantages of Moodle over state-of-the-art methods.