mlsanityData to 14 Oct 2026

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

arXiv 2511.21048 ↗First posted 26 Nov 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 56 references that are in the map. First posted on arXiv 26 Nov 2025.

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References · 22

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  12. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
10 more
  1. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  2. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  3. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 81
  4. 2020CNN-based Density Estimation and Crowd Counting: A Surveycited by 43
  5. 2022A Survey on Heterogeneous Federated Learningcited by 42
  6. 2022Vertical Federated Edge Learning with Distributed Integrated Sensing and Communicationcited by 11
  7. 2022DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learningcited by 4
  8. 2023CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Networkcited by 2
  9. 2024RSSI-Assisted CSI-Based Passenger Counting with Multiple Wi-Fi Receiverscited by 1
  10. 2024Wall-Proximity Matters: Understanding the Effect of Device Placement with Respect to the Wall for Indoor Wi-Fi Sensingcited by 1

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