Communication-Efficient and Privacy-Preserving Aggregation in Federated Learning With Adaptability
Bibliographic record
Abstract
Federated Learning (FL) aims to protect data privacy while aggregating models. Existing works rarely focus simultaneously on the three issues of communication efficiency, privacy, and utility, which are the three main challenges facing FL. Specifically, sensitive information about the training data can still be inferred from the model parameters shared in FL. In recent years, Differential Privacy (DP) has been applied in FL to protect data privacy. The challenge of implementing DP in FL lies in the detrimental impact of differential privacy noise on model accuracy. The DP noise affects the convergence of the model, leading to additional communication overhead. Moreover, considering the inherently high communication costs of FL, FL process can be inefficient or even infeasible. In view of these, we propose a novel Differentially Private Federated Learning (DPFL) scheme named Adap-FedITK, which aims to achieve low communication overhead and high model accuracy while guaranteeing client-level DP. Specifically, we dynamically adjust the gradient clipping threshold for different clients in each round, based on the heterogeneity of gradients. This approach aims to mitigate the negative impact of DP and achieve a privacy-utility trade-off. To alleviate the high communication overhead problem in FL, we introduce an improved Top-k algorithm, which utilizes sparsity and quantization to compress the model eliminates communication redundancy, and it also integrates coding techniques to further reduce communication. Extensive experimental results demonstrate that our method achieves the privacy-utility trade-off and improves communication efficiency while ensuring client-level DPFL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.000 | 0.001 |
| 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".