# ZK-SenseLM: Verifiable Large-Model Wireless Sensing with Selective Abstention and Zero-Knowledge Attestation

arXiv: https://arxiv.org/abs/2510.25677 · HTML: https://mlsanity.com/paper/2510.25677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 74 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning](https://mlsanity.com/paper/2502.18535/) (2025)
- [Learning from Heterogeneity: Generalizing Dynamic Facial Expression Recognition via Distributionally Robust Optimization](https://mlsanity.com/paper/2507.15765/) (2025)
- [WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework](https://mlsanity.com/paper/2402.00822/) (2024)
- [ReSup: Reliable Label Noise Suppression for Facial Expression Recognition](https://mlsanity.com/paper/2305.17895/) (2023)
- [An Overview on IEEE 802.11bf: WLAN Sensing](https://mlsanity.com/paper/2310.17661/) (2023)
- [FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning](https://mlsanity.com/paper/2506.01001/) (2025)
- [Accelerating Mixture-of-Expert Inference with Adaptive Expert Split Mechanism](https://mlsanity.com/paper/2509.08342/) (2025)
- [Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization](https://mlsanity.com/paper/2501.12388/) (2024)
- [Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning](https://mlsanity.com/paper/2506.05977/) (2025)
- [Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attacks](https://mlsanity.com/paper/2404.15587/) (2024)
- [Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data](https://mlsanity.com/paper/2509.08409/) (2025)
- [FreeSense:Indoor Human Identification with WiFi Signals](https://mlsanity.com/paper/1608.03430/) (2016)
- [Wi-Fi CSI based Behavior Recognition: From Signals, Actions to Activities](https://mlsanity.com/paper/1712.00146/) (2017)
- [zk-IoT: Securing the Internet of Things with Zero-Knowledge Proofs on Blockchain Platforms](https://mlsanity.com/paper/2402.08322/) (2024)

## Abstract (arXiv)

ZK-SenseLM is a secure and auditable wireless sensing framework that pairs a large-model encoder for Wi-Fi channel state information (and optionally mmWave radar or RFID) with a policy-grounded decision layer and end-to-end zero-knowledge proofs of inference. The encoder uses masked spectral pretraining with phase-consistency regularization, plus a light cross-modal alignment that ties RF features to compact, human-interpretable policy tokens. To reduce unsafe actions under distribution shift, we add a calibrated selective-abstention head; the chosen risk-coverage operating point is registered and bound into the proof. We implement a four-stage proving pipeline: (C1) feature sanity and commitment, (C2) threshold and version binding, (C3) time-window binding, and (C4) PLONK-style proofs that the quantized network, given the committed window, produced the logged action and confidence. Micro-batched proving amortizes cost across adjacent windows, and a gateway option offloads proofs from low-power devices. The system integrates with differentially private federated learning and on-device personalization without weakening verifiability: model hashes and the registered threshold are part of each public statement. Across activity, presence or intrusion, respiratory proxy, and RF fingerprinting tasks, ZK-SenseLM improves macro-F1 and calibration, yields favorable coverage-risk curves under perturbations, and rejects tamper and replay with compact proofs and fast verification.