Konsep Proses Pemesinan Berkelanjutan

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Być dzieckiem we współczesnej Polsce – szkic demograficzny

The binding sites of circLARP4 with miRNAs. a Schematic representation of potential binding sites of miRNAs with circLARP4. b The effects of miR-424 mimic or inhibitor on the expression level of circLARP4 in HCG-27 or MKN-28 cell line indicated by qRT-PCR. c The binding sites of wild type or mutant circLARP4 3’UTR with miR-424.-5p. d qRT-PCR analysis of the expression levels of LATS1 and YAP after transfection with circLARP4 + miR-424 in HGC-27 cells or si-circLARP4 + miR-424 inhibitor in MKN-28 cells. e the luciferase activity of wild type LATS1 3’UTR was examined by co-transfection with miR-424 mimic + circLARP4 in HGC-27 cells. f the luciferase activity of wild type LATS1 3’UTR was detected by co-transfection with miR-424 inhibitor + si-circLARP4 in MKN-28 cells. *P < 0.05; **P < 0.01. (PDF 2681 kb

Federated learning for training model parameters and hyperparameters.

Federated learning for training model parameters and hyperparameters.</p

Comparing the F1 score of different baseline models.

Comparing each global iteration with a fixed pruning rate of 50 with 150 epochs iteration.</p

Illustration of the process of the intrusion detection model.

Firstly, the local private data is grouped, and then these data are formatted into a time series format to meet the training requirements of the time series model. The formatted data is then inputted into a Multilayer Perceptron (MLP) to generate the final detection results.</p

Communication and time cost on FL vehicle-level training.

Communication and time cost on FL vehicle-level training.</p

FLDP intrusion detection framework and model pruning process.

FLDP intrusion detection framework and model pruning process.</p

Experimental setup.

With the continuous development of vehicular ad hoc networks (VANET) security, using federated learning (FL) to deploy intrusion detection models in VANET has attracted considerable attention. Compared to conventional centralized learning, FL retains local training private data, thus protecting privacy. However, sensitive information about the training data can still be inferred from the shared model parameters in FL. Differential privacy (DP) is sophisticated technique to mitigate such attacks. A key challenge of implementing DP in FL is that non-selectively adding DP noise can adversely affect model accuracy, while having many perturbed parameters also increases privacy budget consumption and communication costs for detection models. To address this challenge, we propose FFIDS, a FL algorithm integrating model parameter pruning with differential privacy. It employs a parameter pruning technique based on the Fisher Information Matrix to reduce the privacy budget consumption per iteration while ensuring no accuracy loss. Specifically, FFIDS evaluates parameter importance and prunes unimportant parameters to generate compact sub-models, while recording the positions of parameters in each sub-model. This not only reduces model size to lower communication costs, but also maintains accuracy stability. DP noise is then added to the sub-models. By not perturbing unimportant parameters, more budget can be reserved to retain important parameters for more iterations. Finally, the server can promptly recover the sub-models using the parameter position information and complete aggregation. Extensive experiments on two public datasets and two F2MD simulation datasets have validated the utility and superior performance of the FFIDS algorithm.</div

Communication cost of different models on different datasets and pruning rates.

Communication cost of different models on different datasets and pruning rates.</p

Comparing the F1 score of different baseline models.

Comparing by varying privacy budgets from 6 to 16 with 150 epochs iteration.</p