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Multimodal data, which can comprehensively perceive and recognize thephysical world, has become an essential path towards general artificialintelligence. However, multimodal large models trained on public datasets oftenunderperform in specific industrial domains. This paper proposes a multimodalfederated learning framework that enables multiple enterprises to utilizeprivate domain data to collaboratively train large models for vertical domains,achieving intelligent services across scenarios. The authors discuss in-depththe strategic transformation of federated learning in terms of intelligencefoundation and objectives in the era of big model, as well as the newchallenges faced in heterogeneous data, model aggregation, performance and costtrade-off, data privacy, and incentive mechanism. The paper elaborates a casestudy of leading enterprises contributing multimodal data and expert knowledgeto city safety operation management , including distributed deployment andefficient coordination of the federated learning platform, technicalinnovations on data quality improvement based on large model capabilities andefficient joint fine-tuning approaches. Preliminary experiments show thatenterprises can enhance and accumulate intelligent capabilities throughmultimodal model federated learning, thereby jointly creating an smart citymodel that provides high-quality intelligent services covering energyinfrastructure safety, residential community security, and urban operationmanagement. The established federated learning cooperation ecosystem isexpected to further aggregate industry, academia, and research resources,realize large models in multiple vertical domains, and promote the large-scaleindustrial application of artificial intelligence and cutting-edge research onmultimodal federated learning.

Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models