Fed-Siamese: Few-Shot RFID Human Activity Recognition With Federated Meta-Learning

In recent years, wireless radio-frequency identification (RFID) technology has shown great potential in human activity recognition (HAR) due to its passive sensing capability, low deployment cost, and strong privacy protection. However, before its widespread application in real-world scenarios, challenges such as heterogeneous data distributions, scarcity of labeled samples, and data security must be addressed. In this paper, we propose Fed-Siamese, a federated meta-learning based HAR system designed to overcome these challenges through several novel contributions. To address the challenge of data distribution heterogeneity across clients, we first model each client’s activity recognition task as an independent learning problem. Building on this, we propose a federated training algorithm and a trusted parameter aggregation strategy based on KL divergence, all within the framework of federated learning and the Reptile meta-learning optimization algorithm. Our approach enables the distributed training of a Siamese network, thereby mitigating the risk of data leakage inherent in centralized batch training. Furthermore, we design a personalized fusion fine-tuning algorithm to balance adaptation efficiency and recognition performance, enabling the system to quickly adapt to new clients using a few labeled samples. Experimental results show that the proposed system outperforms the baselines in recognition performance on both RFID activity datasets, while maintaining stable model convergence under byzantine and backdoor attacks.