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.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.