A Fine-Grained Differentially Private Federated Learning Against Leakage From Gradients
Bibliographic record
Abstract
Federated learning (FL) enables data owners to train a global model with shared gradients while keeping private training data locally. However, recent research demonstrated that the adversary may infer private training data of clients from the exchanged local gradients, e.g., having deep leakage from gradients (DLGs). Many existing privacy-preserving approaches take usage of differential privacy (DP) to guarantee privacy. Nevertheless, the widely used privacy budget of DP (e.g., evenly distribution) leads to a sharp decline of model accuracy. To improve the model accuracy, some schemes only consider allocating the privacy budget to the fully connected layers. However, we reveal that the adversary may still reconstruct the private training data by adopting the DLG attack with the gradients of convolutional layers. In this article, we propose a fine-grained DP federated learning (DPFL) scheme, which guarantees privacy and remains high model performance simultaneously. Specifically, inspired by the methods that measure the importance of layers in deep learning, we propose a fine-grained method to allocate noise according to the importance value of layers in order to remain high model performance. Besides, we combine an active client selection strategy with DPFL and perform fine-tuning with a public data set on the server to further ensure the model performance. We evaluate DPFL under both independent and identically distributed (i.i.d) and non-i.i.d data settings to show that our method can achieve similar accuracy as the plain FL (e.g., FedAvg). We also demonstrate that our DPFL can resist the DLG attack to verify its privacy guarantee.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".