ML-ECS: A Collaborative Multimodal Learning Framework for Edge-Cloud Synergies
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917275499495424 |
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| author | Liu, Yuze Chu, Shibo Zhang, Tiehua Zhou, Hao Shen, Zhishu Wang, Jinze Qi, Jianzhong Xia, Feng |
| author_facet | Liu, Yuze Chu, Shibo Zhang, Tiehua Zhou, Hao Shen, Zhishu Wang, Jinze Qi, Jianzhong Xia, Feng |
| contents | Edge-cloud synergies provide a promising paradigm for privacy-preserving deployment of foundation models, where lightweight on-device models adapt to domain-specific data and cloud-hosted models coordinate knowledge sharing. However, in real-world edge environments, collaborative multimodal learning is challenged by modality heterogeneity (different modality combinations across domains) and model-structure heterogeneity (different modality-specific encoders/fusion modules. To address these issues, we propose ML-ECS, a collaborative multimodal learning framework that enables joint training between a server-based model and heterogeneous edge models. This framework consists of four components: (1) cross-modal contrastive learning (CCL) to align modality representations in a shared latent space, (2) adaptive multimodal tuning (AMT) to preserve domain-specific knowledge from local datasets, (3) modality-aware model aggregation (MMA) to robustly aggregate while mitigating noise caused by missing modalities, and (4) SLM-enhanced CCL (SE-CCL) to facilitate bidirectional knowledge transfer between cloud and edge. Experimental results on various multimodal tasks show that \pname consistently outperform state-of-the-art baselines under varying modality availability, achieving improvements of 5.44% to 12.08% in Rouge-LSum and improving both client- and server-side performance. In addition, by communicating only low-rank LoRA parameters and fused representations, ML-ECS achieves high communication efficiency, requiring only 0.65% of the total parameter volume. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14107 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ML-ECS: A Collaborative Multimodal Learning Framework for Edge-Cloud Synergies Liu, Yuze Chu, Shibo Zhang, Tiehua Zhou, Hao Shen, Zhishu Wang, Jinze Qi, Jianzhong Xia, Feng Distributed, Parallel, and Cluster Computing Edge-cloud synergies provide a promising paradigm for privacy-preserving deployment of foundation models, where lightweight on-device models adapt to domain-specific data and cloud-hosted models coordinate knowledge sharing. However, in real-world edge environments, collaborative multimodal learning is challenged by modality heterogeneity (different modality combinations across domains) and model-structure heterogeneity (different modality-specific encoders/fusion modules. To address these issues, we propose ML-ECS, a collaborative multimodal learning framework that enables joint training between a server-based model and heterogeneous edge models. This framework consists of four components: (1) cross-modal contrastive learning (CCL) to align modality representations in a shared latent space, (2) adaptive multimodal tuning (AMT) to preserve domain-specific knowledge from local datasets, (3) modality-aware model aggregation (MMA) to robustly aggregate while mitigating noise caused by missing modalities, and (4) SLM-enhanced CCL (SE-CCL) to facilitate bidirectional knowledge transfer between cloud and edge. Experimental results on various multimodal tasks show that \pname consistently outperform state-of-the-art baselines under varying modality availability, achieving improvements of 5.44% to 12.08% in Rouge-LSum and improving both client- and server-side performance. In addition, by communicating only low-rank LoRA parameters and fused representations, ML-ECS achieves high communication efficiency, requiring only 0.65% of the total parameter volume. |
| title | ML-ECS: A Collaborative Multimodal Learning Framework for Edge-Cloud Synergies |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2602.14107 |