Multimodal continual federated learning for human activity recognition: Trends and challenges

Human Activity Recognition (HAR) has become increasingly relevant due to the proliferation of sensing devices and their applications in healthcare, smart environments, and human–machine interaction, yet building reliable systems remains challenging in real-world conditions, particularly when integrating wearable and ambient data in intelligent environments. Recent approaches have explored multimodal learning to fuse heterogeneous sensor data, federated learning to preserve data privacy, and continual learning to incrementally adapt to new activities and contexts. This work presents a focused mini-review on the intersection of Multimodal Learning, Federated Learning, and Continual Learning applied to HAR, emphasizing how these paradigms can complement each other to enable more realistic, scalable, and privacy-preserving systems. The review synthesizes recent advances across these domains, highlighting fusion strategies, personalization methods, incremental learning techniques, and federated protocols. The study surveys state-of-the-art approaches, analyzes common challenges, and categorizes contributions along architectural, algorithmic, and deployment axes. The works reviewed indicate an increasing trend toward adaptive fusion mechanisms, generative replay, local personalization, and communication-efficient optimization as means to mitigate catastrophic forgetting, data heterogeneity, and modality imbalance. Finally, this review outlines research opportunities for multimodal federated continual HAR, identifies unresolved trade-offs among privacy, adaptability, and scalability, and suggests future directions, such as standardized benchmarks and interdisciplinary collaboration, to bridge laboratory settings and real-world deployment.