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

A Case for Leveraging 802.11p for Direct Phone-to-Phone Communications

WiFi cannot effectively handle the demands of device-to-device communication between phones, due to insufficient range and poor reliability. We make the case for using IEEE 802.11p DSRC instead, which has been adopted for vehicle-to-vehicle communications, providing lower latency and longer range. We demonstrate a prototype motivated by a novel fabrication process that deposits both III-V and CMOS devices on the same die. In our system prototype, the designed RF front-end is interfaced with a baseband processor on an FPGA, connected to Android phones. It consumes 0.02uJ/bit across 100m assuming free space. Application-level power control dramatically reduces power consumption by 47-56%.Singapore-MIT Alliance for Research and TechnologyAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

A radiomics model based on preoperative gadoxetic acid–enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma

BackgroundPost-hepatectomy liver failure (PHLF) is a fatal complication after liver resection in patients with hepatocellular carcinoma (HCC). It is of clinical importance to estimate the risk of PHLF preoperatively.AimsThis study aimed to develop and validate a prediction model based on preoperative gadoxetic acid–enhanced magnetic resonance imaging to estimate the risk of PHLF in patients with HCC.MethodsA total of 276 patients were retrospectively included and randomly divided into training and test cohorts (194:82). Clinicopathological variables were assessed to identify significant indicators for PHLF prediction. Radiomics features were extracted from the normal liver parenchyma at the hepatobiliary phase and the reproducible, robust and non-redundant ones were filtered for modeling. Prediction models were developed using clinicopathological variables (Clin-model), radiomics features (Rad-model), and their combination.ResultsThe PHLF incidence rate was 24% in the whole cohort. The combined model, consisting of albumin–bilirubin (ALBI) score, indocyanine green retention test at 15 min (ICG-R15), and Rad-score (derived from 16 radiomics features) outperformed the Clin-model and the Rad-model. It yielded an area under the receiver operating characteristic curve (AUC) of 0.84 (95% confidence interval (CI): 0.77–0.90) in the training cohort and 0.82 (95% CI: 0.72–0.91) in the test cohort. The model demonstrated a good consistency by the Hosmer–Lemeshow test and the calibration curve. The combined model was visualized as a nomogram for estimating individual risk of PHLF.ConclusionA model combining clinicopathological risk factors and radiomics signature can be applied to identify patients with high risk of PHLF and serve as a decision aid when planning surgery treatment in patients with HCC

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot

STUDIES ON THE GLOBAL CONVERGENCE OF GRADIENT DESCENT FOR OVER-PARAMETERIZED MODELS USING OPTIMAL TRANSPORT: COMPUTATIONAL OPTIMAL TRANSPORT PROJECT (MVA)

This is the project report of MVA Master 1 course Computational Optimal Transport on the studies of [1]: On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport and [2]: Sparse Optimization on Measures with Over-parameterized Gradient Descen writen by Lénaïc Chizat and Francis Bach. The results of [1] are qualitative while the [2]'s are more quantitative. The [1] aims to explain when and why the non-convex particle gradient descent finds global minima by studying the many-particle limit of the gradient flow. The [2] studies almost the same optimization problem as [1] with a compact d-dimensional Riemannian mani-fold parameter space. One of the biggest relevant interest of [1] is to give a convergence analysis of the optimization problem of neural network (at least the 2-layer one). The [1] does not provide any new algorithm while the [2] propose a Conic Particle Gradient Descent algorithm with retractions on a Riemannian manifold. The contribution of [1] is on the theoretical side: it gives some qualitative convergence theorems on Wasserstein gradient flow. As the particle gradient flow is a particular case of the Wasserstein one and the gradient descent method is only a discretization of the differential equation of the particle gradient flow with fixed particle number, this contribution could be served in the convergence analysis of more complicated deep neural network. On the practical side, the [1] only shows some simple numerical illustrations, for example the convergence of gradient descent of a 2-layer ReLU network with different numbers of particles. In this project, I focus on [1]

Convergence of iterated empirical random measures

This article discusses the convergence of iterated random empirical measures. The result could be served as an alternative modelization of Sampling Importance Resampling. Traditionally, Sampling Importance Resampling is modelized with conditioning. An analysis of convergence modelized by random measures is given here

The complete chloroplast genome of Impatiens mengtszeana (Balsaminaceae), an endemic species in China

Impatiens mengtszeana is an endemic species in China. In this study, the complete chloroplast genome of I. mengtszeana was sequenced and analyzed. The total chloroplast genome size of I. mengtszeana is 152,928 bp, including a pair of inverted repeat regions (IRs, 26,007 bp) separated by a large single copy (LSC, 83,722 bp) region and a small single copy region (SSC, 17,192 bp). The whole chloroplast genome contains 89 protein-coding genes (PCGs), 37 transfer RNA genes (tRNAs), and eight ribosomal RNA genes (rRNAs). According to the phylogenetic topologies, I. mengtszeana was closely related to I. hawkeri

Firefly-based Facial Expression Recognition: Extended Abstract

Automatic facial expression recognition plays an important role in various application domains such as medical imaging, surveillance and human-robot interaction. This research presents a novel facial expression recognition system with modified Local Binary Patterns (LBP) for feature extraction and a modified firefly algorithm (FA) for feature optimization. First, in order to deal with illumination, scaling and rotation variations, we propose a horizontal, vertical and diagonal neighborhood LBP to extract initial discriminative facial features. Then a modified FA is proposed to reduce the dimensionality of the extracted facial features. This FA variant employs Cauchy and Levy distributions to further mutate the best solution identified by the FA to increase exploration in the search space and avoid premature convergence. The overall system is evaluated using two facial expression databases (i.e. CK.+, and MMI). The proposed system outperforms other heuristic search algorithms such as Genetic Algorithm, Particle Swarm Optimization, and other existing state-of-the-art facial expression recognition research, significantly