Federated Multi-Modal Learning Across Distributed Devices

Multi-modal sensing systems generate rich physiological and motion data that can support real-time classification, anomaly detection, and personalized analytics. Traditional cloud-centric machine learning pipelines require transmitting raw sensor streams to remote servers, creating challenges related to privacy, bandwidth usage, and latency. This paper presents a federated multi-modal learning framework that enables distributed devices to collaboratively train a shared model without exposing raw data. The framework integrates compact temporal convolution and sequence-modeling components for on-device training, combined with differential privacy and Top-K gradient sparsification to reduce information leakage and communication overhead. A three-tier architecture coordinates local processing, intermediate aggregation, and global optimization while maintaining consistent model quality under heterogeneous sensor conditions. Experiments using multi-modal datasets demonstrate that the proposed approach achieves 93.1% accuracy, reduces communication cost by 68% compared to classic federated learning, and sustains 18 to 22 ms inference latency on constrained hardware. These results show that federated multi-modal learning can provide scalable, privacy-conscious intelligence across large networks of distributed devices.