Towards Efficient and Privacy-Preserving Federated Learning for HMM Training
Bibliographic record
Abstract
The hidden Markov model (HMM) has played a pivotal role in various IoT applications due to its ability to model time-varying sequences. Since the datasets usually live in isolated islands and their privacy naturally demands to be seriously considered, the HMM should be trained in a privacy-preserving manner. A typical HMM training framework is federated learning, in which a federated server and many data owners collaboratively train an HMM without revealing data owners' data to the federated server and the trained model to data owners. Since existing HMM training schemes are computationally intensive, we propose an efficient and privacy-preserving federated learning scheme for HMM training to address the efficiency issue in this paper. First, we transform all HMM training computations into matrices- and vectors-based computations over real domains. Then, we introduce our federated HMM training scheme by applying matrix encryption to protect the HMM training privacy. After that, we show that our scheme is privacy-preserving through a rigorous analysis on the security of our scheme. We illustrate that our scheme is efficient through extensive experimental evaluation on the performance of our scheme.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".