Restrictively Self-sampled and Compressed Local Differential Privacy in Federated Learning
Bibliographic record
Abstract
Abstract As a popular machine learning framework, federated learning (FL) enables clients to conduct cooperative training without sharing data, thus having higher security than conventional machine learning. However, by sharing parameters in the federated learning process, the attacker can still obtain private information from the sensitive data of participants by reverse parsing. Recently, local differential privacy (LDP) has worked well in preserving privacy for federated learning. However, it faces the inherent problem of balancing privacy, model performance, and algorithm efficiency. In this paper, we propose a novel local differential privacy method in federated learning (SLDP-FL), which achieves the privacy amplification effect by the client self-sampling and provides compressed and private parameters in each iteration by a compressed LDP mechanism. Thereby, it improves the model performance as well as efficiency observably.Moreover, we theoretically analyze the relationship between the model accuracy and client self-sampling probability. A restrictive client self-sampling technology is proposed, which eliminates the randomness of self-sampling probability settings in existing studies and improves the utilization of the federated system. Comprehensive experiments on MNIST and Fashion-MNIST datasets show that the SLDP-FL optimizes the existing federated learning framework through compression mechanism and self-sampling technique with restrictive probability since it is superior to the current algorithms' accuracy and convergence and communication efficiency.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.030 | 0.455 |
| Research integrity | 0.001 | 0.008 |
| 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; both teacher heads agree on what is shown here.
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".