| 1. |
- Meng, Qinglai, et al.
(author)
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Rapid personalized AMR diagnostics using two-dimensional antibiotic resistance profiling strategy employing a thermometric NDM-1 biosensor
- 2021
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In: Biosensors and Bioelectronics. - : Elsevier BV. - 0956-5663. ; 193
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Journal article (peer-reviewed)abstract
- Antimicrobial resistance (AMR) threatens global public health and modern surgical medicine. Expression of β-lactamase genes is the major mechanism by which pathogens become antibiotic resistant. Pathogens expressing extended spectrum β-lactamases (ESBL) and carbapenemases (CP) are especially difficult to treat and are associated with increased hospitalization and mortality rates. Despite considerable effort, identification of ESBLs and CPs in a clinically relevant timeframe remains challenging. In this study, a two-dimensional AMR profiling assay strategy was developed employing panels of antibiotics (penicillins, cephamycins, oximino-cephalosporins and carbapenems) and β-lactamases inhibitors (avibactam and EDTA). The assay required the development of a novel biosensor that employed New Delhi metallo-β-lactamase-1 (NDM-1) as the sensing element. Functionally probing β-lactamase activity using substrates and inhibitors combinatorically increased the informational content that enabled the development of assays capable of simultaneous, differential identification of multiple β-lactamases expressed in a single bacterial isolate. More specifically, the assay enabled the simultaneous identification of ESBL and CP in mock samples, as well as in an engineered construct which co-expressed these β-lactamases. The NDM-1 biosensor assay was 16 times and 8 times more sensitive than the ESBL Nordmann/Dortet/Poirel (NDP) and Carba Nordmann/Poirel (NP) assays, respectively. In a retrospective study, NDM-1 biosensor assays were able to differentially identify ESBLs, metallo-CPs and serine-CPs β-lactamases in 23 clinical isolates with 100% accuracy. An assay algorithm was developed which accelerated data analytics reducing turnaround to <1 h. The assay strategy integrated with AI-based data analytics has the potential to provide physicians with a comprehensive readout of patient AMR status.
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| 2. |
- Yi, Yuhao, et al.
(author)
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Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers
- 2024
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In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. - : Association for the Advancement of Artificial Intelligence (AAAI). ; , s. 16469-16477
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Conference paper (peer-reviewed)abstract
- Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure resilience against Byzantine machines in distributed learning is resilient aggregation mechanisms. Although abundant resilient aggregation rules have been proposed, they are designed in ad-hoc manners, imposing extra barriers on comparing, analyzing, and improving the rules across performance criteria. This paper studies near-optimal aggregation rules using clustering in the presence of outliers. Our outlier-robust clustering approach utilizes geometric properties of the update vectors provided by workers. Our analysis show that constant approximations to the 1-center and 1-mean clustering problems with outliers provide near-optimal resilient aggregators for metric-based criteria, which have been proven to be crucial in the homogeneous and heterogeneous cases respectively. In addition, we discuss two contradicting types of attacks under which no single aggregation rule is guaranteed to improve upon the naive average. Based on the discussion, we propose a two-phase resilient aggregation framework. We run experiments for image classification using a non-convex loss function. The proposed algorithms outperform previously known aggregation rules by a large margin with both homogeneous and heterogeneous data distributions among non-faulty workers. Code and appendix are available at https://github.com/jerry907/AAAI24-RASHB.
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| 3. |
- Dawoud, Mohammed M., et al.
(author)
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Differentially Private Set-Based Estimation Using Zonotopes
- 2023
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In: 2023 European Control Conference, ECC 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
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Conference paper (peer-reviewed)abstract
- For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise from disclosing sensitive measurement signals to a cloud estimator that predicts the system state. To solve this issue, we propose a differentially private set-based estimation protocol that preserves the privacy of the measurement signals. Compared to existing research, our approach achieves less privacy loss and utility loss using a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach. Zonotopes, a less conservative form of set representation, are used to represent estimation sets, giving set operations a computational advantage. The privacy-preserving noise anonymizes the centers of these estimated zonotopes, concealing the precise positions of the estimated zonotopes.
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| 4. |
- Fan, Zhenan, et al.
(author)
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Improving Fairness for Data Valuation in Horizontal Federated Learning
- 2022
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In: 38th IEEE International Conference on Data Engineering, ICDE 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2440-2453
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Conference paper (peer-reviewed)abstract
- Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the participation of data owners. To sustain and encourage data owners' participation, it is crucial to fairly evaluate the quality of the data provided by the data owners as well as their contribution to the final model and reward them correspondingly. Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure for data value under the framework of federated learning that satisfies many desired properties for data valuation. However, there are still factors of potential unfairness in the design of federated Shapley value because two data owners with the same local data may not receive the same evaluation. We propose a new measure called completed federated Shapley value to improve the fairness of federated Shapley value. The design depends on completing a matrix consisting of all the possible contributions by different subsets of the data owners. It is shown under mild conditions that this matrix is approximately low-rank by leveraging concepts and tools from optimization. Both theoretical analysis and empirical evaluation verify that the proposed measure does improve fairness in many circumstances.
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| 5. |
- Li, Zishuo, et al.
(author)
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Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors
- 2023
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In: IFAC-PapersOnLine. - : Elsevier BV. ; , s. 270-275
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Conference paper (peer-reviewed)abstract
- This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.
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| 6. |
- Liu, Changxin, et al.
(author)
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Distributed empirical risk minimization with differential privacy
- 2024
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In: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 162
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Journal article (peer-reviewed)abstract
- This work studies the distributed empirical risk minimization (ERM) problem under differential privacy (DP) constraint. Standard distributed algorithms achieve DP typically by perturbing all local subgradients with noise, leading to significantly degenerated utility. To tackle this issue, we develop a class of private distributed dual averaging (DDA) algorithms, which activates a fraction of nodes to perform optimization. Such subsampling procedure provably amplifies the DP guarantee, thereby achieving an equivalent level of DP with reduced noise. We prove that the proposed algorithms have utility loss comparable to centralized private algorithms for both general and strongly convex problems. When removing the noise, our algorithm attains the optimal O(1/t) convergence for non-smooth stochastic optimization. Finally, experimental results on two benchmark datasets are given to verify the effectiveness of the proposed algorithms.
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| 7. |
- Liu, Changxin, et al.
(author)
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Event-Triggered Distributed Nonconvex Optimization with Progress-Based Threshold
- 2023
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In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 309-314
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Conference paper (peer-reviewed)abstract
- This work studies the distributed nonconvex optimization problem in bandwidth-limited communication environments. We develop a communication-efficient algorithm based on the gradient-tracking based distributed optimization method, where each computation node is equipped with a new event-triggered communication scheduler. Such scheduler approves the broadcasting only when the innovation of exchanged variables exceeds the change of decision variables in two consecutive updates. Compared to the conventional scheduler with time-dependent vanishing thresholds, the proposed one adapts better to the optimization dynamics and thus leads to more significant communication reduction. Finally, we prove the convergence of the algorithm and illustrate its performance via numerical examples.
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| 8. |
- Liu, Changxin, et al.
(author)
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Private Stochastic Dual Averaging for Decentralized Empirical Risk Minimization
- 2022
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In: 9th IFAC Conference on Networked Systems NECSYS 2022Zürich, Switzerland, 5–7 July 2022. - : Elsevier BV. ; , s. 43-48
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Conference paper (peer-reviewed)abstract
- In this work, we study the decentralized empirical risk minimization problem under the constraint of differential privacy (DP). Based on the algorithmic framework of dual averaging, we develop a novel decentralized stochastic optimization algorithm to solve the problem. The proposed algorithm features the following: i) it perturbs the stochastic subgradient evaluated over individual data samples, with which the information about the dataset can be released in a differentially private manner; ii) it employs hyperparameters that are more aggressive than conventional decentralized dual averaging algorithms to speed up convergence. The upper bound for the utility loss of the proposed algorithm is proven to be smaller than that of existing methods to achieve the same level of DP. As a by-product, when removing the perturbation, the non-private version of the proposed algorithm attains the optimal O(1/t) convergence rate for smooth stochastic optimization. Finally, experimental results are presented to demonstrate the effectiveness of the algorithm.
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| 9. |
- Liu, Changxin, et al.
(author)
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Rate analysis of dual averaging for nonconvex distributed optimization
- 2023
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In: IFAC-PapersOnLine. - : Elsevier BV. ; , s. 5209-5214
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Conference paper (peer-reviewed)abstract
- This work studies nonconvex distributed constrained optimization over stochastic communication networks. We revisit the distributed dual averaging algorithm, which is known to converge for convex problems. We start from the centralized case, for which the change of two consecutive updates is taken as the suboptimality measure. We validate the use of such a measure by showing that it is closely related to stationarity. This equips us with a handle to study the convergence of dual averaging in nonconvex optimization. We prove that the squared norm of this suboptimality measure converges at rate O(1/t). Then, for the distributed setup we show convergence to the stationary point at rate O(1/t). Finally, a numerical example is given to illustrate our theoretical results.
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| 10. |
- Liu, Shichao, et al.
(author)
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一种β-内酰胺类抗生素的酶热检测方法
- 2019
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In: Journal of Shanxi University (Natural Science Edition). ; :2021-02
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Journal article (peer-reviewed)abstract
- The penicillinase thermistor biosensor（Penicillinase sensor） was developed for the rapid monitoring of blood penem antibiotics concentration and rapid identification of extraneous penicillinase in milk on site.However, the wide application of the penicillinase thermistor biosensor was limited due to its intrinsic poor activity to hydrolyze cephem and carbapenem antibiotics.The recently identified carbapenemase New Delhi metallo-beta-lactamase 1（NDM-1） is able to hydrolyze all commercially available β-lactam antibiotics in high efficacy.We coupled the NDM-1 and the enzymatic thermistor biosensor to develop a NDM-1 thermistor biosensor（NDM-1 sensor） by the installment of the enzymatic thermistor with an enzyme column loaded with NDM-1 conjugated CPG beads.The NDM-1 sensor shows high response activity to Piperacillin（PIP）,Ceftriaxone（CTRX）, and Meropenem（MEM）, and the response activity of the NDM-1 sensor to these three β-lactam antibiotics are all Zn2+ dependent.Moreover, the response activity of the NDM-1 sensor to Penicillin G（P）, PIP, Cefazolin（CZO）, CTRX, Cefepime（FEP） and MEM all linearly correlated with antibiotic concentration from 31.25 to 1 000 mg/L.Within pH from 6.0 to 8.0, the optimal response activity of the NDM-1 sensor to P,PIP, CZO, CTRX and FEP are found at pH 6.5, while the optimal response activity of the NDM-1 sensor to MEM is found at pH8.0.These data indicate that the featured activity of NDM-1 was well maintained after conjugation on CPG beads, and NDM-1 sensor is capable to quantitate three classes of β-lactam antibiotics including penem, cephem and carbapenem within a wide concentration range.
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| 11. |
- Meng, Qinglai, et al.
(author)
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Rapid Detection of Multiple Classes of β-Lactam Antibiotics in Blood Using an NDM-1 Biosensing Assay
- 2021
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In: Antibiotics. - : MDPI AG. - 2079-6382. ; 10:9
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Journal article (peer-reviewed)abstract
- Currently, assays for rapid therapeutic drug monitoring (TDM) of β-lactam antibiotics in blood, which might be of benefit in optimizing doses for treatment of critically ill patients, remain challenging. Previously, we developed an assay for determining the penicillin-class antibiotics in blood using a thermometric penicillinase biosensor. The assay eliminates sample pretreatment, which makes it possible to perform semicontinuous penicillin determinations in blood. However, penicillinase has a narrow substrate specificity, which makes it unsuitable for detecting other classes of β-lactam antibiotics, such as cephalosporins and carbapenems. In order to assay these classes of clinically useful antibiotics, a novel biosensor was developed using New Delhi metallo-β-lactamase-1 (NDM-1) as the biological recognition layer. NDM-1 has a broad specificity range and is capable of hydrolyzing all classes of β-lactam antibiotics in high efficacy with the exception of monobactams. In this study, we demonstrated that the NDM-1 biosensor was able to quantify multiple classes of β-lactam antibiotics in blood plasma at concentrations ranging from 6.25 mg/L or 12.5 mg/L to 200 mg/L, which covered the therapeutic concentration windows of the tested antibiotics used to treat critically ill patients. The detection of ceftazidime and meropenem was not affected by the presence of the β-lactamase inhibitors avibactam and vaborbactam, respectively. Furthermore, both free and protein-bound β-lactams present in the antibiotic-spiked plasma samples were detected by the NDM-1 biosensor. These results indicated that the NDM-1 biosensor is a promising technique for rapid TDM of total β-lactam antibiotics present in the blood of critically ill patients.
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| 12. |
- Wang, Yuan, et al.
(author)
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Resilient distributed optimization under mobile malicious attacks
- 2023
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Conference paper (peer-reviewed)abstract
- This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.
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| 13. |
- Wu, Xuyang, et al.
(author)
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Delay-agnostic Asynchronous Coordinate Update Algorithm
- 2023
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In: ICML'23. ; , s. 37582-37606
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Conference paper (peer-reviewed)abstract
- We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes novel asynchronous variants of ADMM and block-coordinate descent as special cases. We prove that DEGAS converges with both bounded and unbounded delays under delay-free parameter conditions. We also validate by theory and experiments that DEGAS adapts well to the actual delays. The effectiveness of DEGAS is demonstrated by numerical experiments on classification problems.
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| 14. |
- Wu, Xuyang, et al.
(author)
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Delay-agnostic Asynchronous Distributed Optimization
- 2023
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In: 2023 62Nd Ieee Conference On Decision And Control, Cdc. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 ; , s. 1082-1087
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Conference paper (peer-reviewed)abstract
- Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed upper bound of delays. Not only is such a delay bound hard to obtain in advance, but it is also large and therefore results in unnecessarily slow convergence. This paper develops asynchronous versions of two distributed algorithms, DGD and DGD-ATC, for solving consensus optimization problems over undirected networks. In contrast to alternatives, our algorithms can converge to the fixed point set of their synchronous counterparts using step-sizes that are independent of the delays. We establish convergence guarantees under both partial and total asynchrony. The practical performance of our algorithms is demonstrated by numerical experiments.
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