Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism

Analysis of Patents Issued in China for Antihyperglycemic Therapies for Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is prevalent, with a dramatic increase in recent years. Moreover, its microvascular and macrovascular complications cause significant societal issues. The demand for new and effective antidiabetic therapies grows with each passing day and motivates organizations and individuals to pay more attention to such products. In this article, we focused on oral antihyperglycemic drugs patented in China and introduced them according to their antihyperglycemic mechanisms. By searching the website of State Intellectual Property Office of the People’s Republic of China (http://www.sipo.gov.cn), 2,500 antihyperglycemic patents for T2DM were identified and analyzed. These consisted of 4 patents for derivatives of herbal extracts (0.2%), 162 patents for herbal extracts (6.5%), 61 compositions for traditional Chinese medicine (TCM) (2.4%), 2,263 patents for synthetic compounds (90.5%), and 10 (0.4%) patents of the combination of synthetic compounds and TCM. As the most common drugs for diabetes mellitus, synthetic compounds can also be classified into several categories according to their working mechanisms, such as insulin secretion promotor agents, insulin sensitizer agents, α-glucosidase inhibitors, and so forth. This article discussed the chemical structure, potential antihyperglycemic mechanism of these antihyperglycemic drugs in patents in China.Expert opinion: Insulin sensitivity and β-cell function could be improved by weight loss to prevent prediabetes into T2DM. However, 40–50% patients with impaired glucose tolerance (IGT) still progress to T2DM, even after successful long-term weight loss.Antihyperglycemic remedies provide a treatment option to improve insulin sensitivity and maintain β-cell function. Combination therapy is the best treatment for diabetes. Combination therapy can reduce the dosage of each single drug option, and avoid the side effects. Drugs with different mechanisms are complementary, and are better adapted to patients with changing conditions. Classical combination therapies include combinations such as sulfonylureas plus biguanides or glucosidase inhibitors, biguanide plus glucosidase inhibitors or insulin sensitizers, insulin treatment plus biguanides or glucosidase inhibitors. The general principle of combination therapy is that two drugs with different mechanisms are selected jointly, and the combination of three types of hypoglycemic drugs is not recommended. After reading a large amount of literature, we have rarely found a case of three oral hypoglycemic agents, which may mean that the combination of three oral hypoglycemic agents is unnecessary and has unpredictable risks. There is no objection to the idea of multi-drug therapy. But multiple drugs can only be used when it shows a significant benefit to the patients. Combined use of multiple antidiabetic drugs poses a risk to patients due to drug interactions and overtreatment

Improved calorimetric particle identification in NA62 using machine learning techniques

International audienceMeasurement of the ultra-rare ${K}^{+}\to {\pi}^{+}\nu \overline{\nu}$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10$^{−5}$ for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10$^{−5}$

Improved calorimetric particle identification in NA62 using machine learning techniques

Measurement of the ultra-rare $K^+\to\pi^+\nu\bar\nu$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of $1.2\times 10^{-5}$ for a pion identification efficiency of 75% in the momentum range of 15-40 GeV/$c$. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of $10^{-5}$

Improved calorimetric particle identification in NA62 using machine learning techniques

International audienceMeasurement of the ultra-rare ${K}^{+}\to {\pi}^{+}\nu \overline{\nu}$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 × 10$^{−5}$ for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10$^{−5}$

Improved calorimetric particle identification in NA62 using machine learning techniques

Measurement of the ultra-rare $K^+\to\pi^+\nu\bar\nu$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of $1.2\times 10^{-5}$ for a pion identification efficiency of 75% in the momentum range of 15$-$40 GeV/$c$. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of $10^{-5}$.Measurement of the ultra-rare $K^+\to\pi^+\nu\bar\nu$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of $1.2\times 10^{-5}$ for a pion identification efficiency of 75% in the momentum range of 15-40 GeV/$c$. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of $10^{-5}$