FedAPA: Federated Learning with Adaptive Prototype Aggregation Toward Heterogeneous Wi-Fi CSI-based Crowd Counting

2025-11-25

AI Summary

In Wi-Fi CSI-based crowd counting, large-scale deployment is hindered by reliance on scene-specific labeled data, while federated learning faces challenges from statistical and system heterogeneity across devices and data distributions. To address these issues, this paper proposes FedAPA, a novel federated learning framework featuring Adaptive Prototype Aggregation (APA)—a similarity-aware mechanism that dynamically generates personalized global prototypes for each client, overcoming the limitations of conventional fixed-weight aggregation. FedAPA further introduces a hybrid local optimization objective integrating classification loss, contrastive representation learning, and knowledge alignment, enabling efficient collaborative training under low communication overhead. Extensive evaluation across six real-world environments demonstrates that FedAPA outperforms state-of-the-art baselines by ≥9.65% in accuracy, +9% in F1-score, −0.29 in MAE, and reduces communication cost by 95.94%.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Learning & Optimization for CV
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Wi-Fi channel state information (CSI)-based sensing provides a non-invasive, device-free approach for tasks such as human activity recognition and crowd counting, but large-scale deployment is hindered by the need for extensive site-specific training data. Federated learning (FL) offers a way to avoid raw data sharing but is challenged by heterogeneous sensing data and device resources. This paper proposes FedAPA, a collaborative Wi-Fi CSI-based sensing algorithm that uses adaptive prototype aggregation (APA) strategy to assign similarity-based weights to peer prototypes, enabling adaptive client contributions and yielding a personalized global prototype for each client instead of a fixed-weight aggregation. During local training, we adopt a hybrid objective that combines classification learning with representation contrastive learning to align local and global knowledge. We provide a convergence analysis of FedAPA and evaluate it in a real-world distributed Wi-Fi crowd counting scenario with six environments and up to 20 people. The results show that our method outperform multiple baselines in terms of accuracy, F1 score, mean absolute error (MAE), and communication overhead, with FedAPA achieving at least a 9.65% increase in accuracy, a 9% gain in F1 score, a 0.29 reduction in MAE, and a 95.94% reduction in communication overhead.

Problem

• Addressing data heterogeneity in federated Wi-Fi sensing systems
• Reducing communication overhead in distributed crowd counting applications
• Improving accuracy for device-free crowd counting using Wi-Fi CSI

Innovation

• Adaptive prototype aggregation assigns similarity-based weights
• Hybrid objective combines classification and contrastive learning
• Personalized global prototypes replace fixed-weight aggregation