{"doi":"10.1145/3715014.3722053","title":"MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT","abstract":"Multimodal sensing systems are increasingly prevalent in various real-world applications. Most existing multimodal learning approaches heavily rely on training with a large amount of synchronized, complete multimodal data. However, such a setting is impractical in real-world IoT sensing applications where data is typically collected by distributed nodes with heterogeneous data modalities, and is also rarely labeled. In this paper, we propose MMBind, a new data binding approach for multimodal learning on distributed and heterogeneous IoT data. The key idea of MMBind is to construct a pseudo-paired multimodal dataset for model training by binding data from disparate sources and incomplete modalities through a sufficiently descriptive shared modality. We also propose a weighted contrastive learning approach to handle domain shifts among disparate data, coupled with an adaptive multimodal learning architecture capable of training models with heterogeneous modality combinations. Evaluations on ten real-world multi-modal datasets highlight that MMBind outperforms state-of-the-art baselines under varying degrees of data incompleteness and domain shift, and holds promise for advancing multimodal foundation model training in IoT applications1.","journal":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","year":2025,"id":555168,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8981,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1336464,"name":"Jason Wu","orcid":"0000-0002-8174-4751","position":1,"is_corresponding":false},{"id":1357652,"name":"Tomoyoshi Kimura","orcid":"0009-0008-4297-5865","position":2,"is_corresponding":false},{"id":1453463,"name":"Yihan Lin","orcid":"0000-0002-5051-4902","position":3,"is_corresponding":false},{"id":1453464,"name":"Gunjan Verma","orcid":"0000-0002-0480-8758","position":4,"is_corresponding":false},{"id":1357654,"name":"Tarek Abdelzaher","orcid":"0000-0003-3883-7220","position":5,"is_corresponding":false},{"id":563489,"name":"Mani Srivastava","orcid":"0000-0002-3782-9192","position":6,"is_corresponding":false},{"id":1330624,"name":"Xiaomin Ouyang","orcid":"0000-0003-0710-0963","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-19T02:54:59.329539Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}