Federated Learning for Privacy-Preserving Multimodal AI  

ABSTRACT:
This chapter discusses Federated Learning as a distributed approach for developing privacy-preserving multimodal AI systems. It explains how organizations, farms, hospitals, laboratories, and industries can collaboratively train AI models while keeping raw multimodal data locally and sharing only model updates. The chapter examines federated multimodal architectures, local and global model training, privacy and security requirements, communication efficiency, aggregation, missing modalities, client reliability, and the privacy–performance trade-off. Applications in agricultural quality assessment, healthcare, manufacturing, and other distributed environments are considered. Future directions include federated foundation models, federated Vision Transformers, adaptive multimodal learning, explainability, model compression, quantization, and energy-efficient federated learning.