Federated Learning for WiFi Fingerprinting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Channel State Information (CSI) based fingerprinting is surfacing as an accurate and robust method of indoor localization. However, the high-dimensional nature of CSI data impedes its adoption in multi access point (AP) systems. To reap the rewards of cooperative localization with privacy and limited system complexity in mind, we propose a federated learning (FL) architecture. Each AP has an individual model and a shared model, where the individual model parameters are unique to each AP and the shared model parameters are communicated to a central server for aggregation. The server averages the models and sends them back to each AP, which use this joint model as a regularization term. To capture the spatio-temporal characteristics of CSI, we propose a convolutional neural network (CNN) as each AP’s individual model and a multi layer perceptron (MLP) as the shared model. Extensive experimental studies verify the superiority of the proposed edge computing approach compared to the exiting methods in the literature. We use commercial off-the-shelf APs collecting CSI data in multiple indoor environments and compare the proposed system to a state-of-the-art deep learning model. Our approach shows significant improvement in the localization accuracy for both individual APs and aggregate predictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it