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Consensus plus Innovations Distributed inference over Networks

IEEE SIGNAL PROCESSING MAGAZINE 2013年第3期30卷 99-109页

作者： Kar, Soummya Moura, Jose M. F. Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

This article presents consensus + innovations inference algorithms that intertwine consensus (local averaging among agents) and innovations (sensing and assimilation of new observations). These algorithms are of importance in many scenarios that involve cooperation and interaction among a large number of agents with no centralized coordination. The agents only communicate locally over sparse topologies and sense new observations at the same rate as they communicate. This stands in sharp contrast with other distributed inference approaches, in which interagent communications are assumed to occur at a much faster rate than agents can sense (sample) the environment so that, in between measurements, agents may iterate enough times to reach a decision-consensus before a new measurement is made and assimilated. While optimal design of distributed inference algorithms in stochastic time-varying scenarios is a hard (often intractable) problem, this article emphasizes the design of asymptotically (in time) optimal distributed inference approaches, i.e., distributed algorithms that achieve the asymptotic performance of the corresponding optimal centralized inference approach (with instantaneous access to the entire network sensed information at all times). Consensus + innovations algorithms extend consensus in nontrivial ways to mixed-scale stochastic approximation algorithms, in which the time scales (or weighting) of the consensus potential (the potential for distributed agent collaboration) and of the innovation potential (the potential for local innovations) are suitably traded for optimal performance. This article shows why this is needed and what the implications are, giving the reader pointers to new methodologies that are useful in their own right and in many other contexts.

关键词： Complex networks Learning systems inference algorithms Technological innovation Algorithm design and analysis Approximation algorithms Distributed algorithms Adaptation models

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Stable Knowledge Tracing Using Causal inference

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2024年 17卷 124-134页

作者： Zhu, Jia Ma, Xiaodong Huang, Changqin Zhejiang Normal Univ Key Lab Intelligent Educ Technol & Applicat Zhejia Jinhua 321004 Peoples R China

Knowledge tracing (KT) for evaluating students' knowledge is an essential task in personalized education. More and more researchers have devoted themselves to solving KT tasks, e.g., deep knowledge tracing (DKT), which can capture more sophisticated representations of student knowledge. Nonetheless, these techniques ignore the reconstruction of the observed input information. Therefore, this leads to poor predictions of students' knowledge, even if the student performed well in the past knowledge state. In this article, we first employ causal inference for explanatory analysis of KT, then propose a learning algorithm for stable KT based on the analysis outcomes. The proposed approach aims to achieve stable KT by constructing global balanced weights that facilitate estimating feature influence and assessing causal relationships between individual variables and outcome variables. We have proved the approach has effective in accuracy and interpretability through extensive experimentation on real-world datasets. In conclusion, this article has methodological implications for the stable assessment of students' knowledge and provides a reference for personalization and use of intelligence in the educational teaching process.

关键词： Analytical models Education Predictive models Prediction algorithms inference algorithms Stability analysis Robustness Causal inference deep knowledge tracing (DKT) learning sciences stable knowledge tracing (KT)

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From Network inference Errors to Utility Suboptimality: How Much Is the Impact?

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第6期9卷 4075-4086页

作者： Li, Shijing Lan, Tian George Washington Univ Washington DC 20052 USA

Accurate and timely network inference, known as network tomography, is a vital ingredient in efficient network operation, supporting more sophisticated and ambitious traffic optimization algorithms. In practice, however, precise knowledge of the dynamic internal network bottlenecks/states can be impossible to obtain under restricted network visibility on network edge where only end-to-end measurements are available without cooperation of internal network components. In this paper, we acknowledge that network tomography on the edge is often imperfect, leading to dynamic, time-varying inference errors that could change from iteration to iteration on the same time-scale as distributed network optimization algorithms. We quantify the impact of such imperfect bottleneck/state inference on algorithm convergence and optimality. In particular, we show that under arbitrary, bounded inference errors (belonging to three common classes including absent and incorrect bottlenecks, and inaccurate capacity), the solution of the distributed optimization algorithm still converges to a bounded neighborhood of the optimal solution. The resulted optimality gap is quantified in closed form and shown to be proportional to average inference errors. These results are evaluated using extensive network simulations and on real-world IoT data sets. The work provide a theoretical support for understanding the impact of imperfect inference on distributed network optimization.

关键词： inference algorithms Heuristic algorithms Optimization Tomography Routing Network topology Knowledge engineering Network inference network optimization utility optimization

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NETWORK TOPOLOGIES: inference, MODELING, AND GENERATION

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2008年第2期10卷 49-70页

作者： Haddadi, Hamed Rio, Miguel Iannaccone, Gianluca Moore, Andrew Mortier, Richard UCL Dept Elect & Elect Engn London WC1E 6BT England Intel Res Cambridge England Univ Cambridge Comp Lab Cambridge CB2 1TN England

Accurate measurement, inference and modeling techniques are fundamental to Internet topology research. Spatial analysis of the Internet is needed to develop network planning, optimal routing algorithms, and failure detection measures. A first step toward achieving such goals is the availability of network topologies at different levels of granularity, facilitating realistic simulations of new Internet systems. The main objective of this survey is to familiarize the reader with research on network topology over the past decade. We study techniques for inference, modeling, and generation of the Internet topology at both the router and administrative levels. We also compare the mathematical models assigned to various topologies and the generation tools based on them. We conclude with a look at emerging areas of research and potential future research directions.

关键词： Network topology Internet IP networks Educational institutions Laboratories Failure analysis Algorithm design and analysis Routing inference algorithms Mathematical model

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Joint Network Topology inference in the Presence of Hidden Nodes

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 2710-2725页

作者： Navarro, Madeline Rey, Samuel Buciulea, Andrei Marques, Antonio G. Segarra, Santiago Rice Univ Dept ECE Houston TX 77005 USA King Juan Carlos Univ Dept Signal Theory & Commun Madrid 28933 Spain

We investigate the increasingly prominent task of jointly inferring multiple networks from nodal observations. While most joint inference methods assume that observations are available at all nodes, we consider the realistic and more difficult scenario where a subset of nodes are hidden and cannot be measured. Under the assumptions that the partially observed nodal signals are graph stationary and the networks have similar connectivity patterns, we derive structural characteristics of the connectivity between hidden and observed nodes. This allows us to formulate an optimization problem for estimating networks while accounting for the influence of hidden nodes. We identify conditions under which a convex relaxation yields the sparsest solution, and we formalize the performance of our proposed optimization problem with respect to the effect of the hidden nodes. Finally, synthetic and real-world simulations provide evaluations of our method in comparison with other baselines.

关键词： Network topology Topology Task analysis Signal processing Signal processing algorithms inference algorithms Symmetric matrices Graph learning network topology inference hidden nodes graph signal processing graph stationarity multi-layer graphs

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QoS-Aware inference Acceleration Using Adaptive Depth Neural Networks

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IEEE ACCESS 2024年 12卷 49329-49340页

作者： Kang, Woochul Incheon Natl Univ Dept Embedded Syst Engn Incheon 22012 South Korea

While deep neural networks (DNNs) have brought revolutions to many intelligent services and systems, the deployment of high-performing models for real-world applications faces challenges posed by resource constraints and diverse operating environments. While existing methods such as model compression combined with inference accelerators have enhanced the efficiency of deep neural networks, they are not dynamically adaptable to dynamically changing resource conditions since they provide static accuracy-efficiency trade-offs. Further, since they are not aware of performance requirements, such as desired inference latency, they are not able to provide robust and effective performance under unpredictable workloads. This paper introduces a holistic solution to address this challenge, consisting of two key components: adaptive depth neural networks and the Quality of Service (QoS)-aware inference accelerator. The adaptive depth neural networks exhibit the ability to scale computation instantly with minimal impact on accuracy, utilizing a novel architectural pattern and training algorithm. Complementing this, the QoS-aware inference accelerator employs a feedback control loop, adapting network depth dynamically to meet desired inference latency. Experimental results demonstrate that the proposed adaptive depth networks outperform non-adaptive counterparts, achieving up to 38% dynamic acceleration via depth adaption, with a marginal accuracy loss of 1.5%. Furthermore, the QoS-aware inference accelerator successfully controls network depth at runtime, ensuring robust performance in unpredictable environments.

关键词： Adaptive systems Quality of service Task analysis Deep learning Adaptation models Training inference algorithms Artificial neural networks Feedback control Real-time systems Energy efficiency inference acceleration deep neural networks feedback control real-time energy efficiency quality-of-service QoS

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Temporal inference of workflow systems based on time Petri nets: Quantitative and qualitative analysis

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2004年第5期19卷 417-442页

作者： Lin, C Qu, Y Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Recently, a significant body of research has been dedicated to the analysis and verification of workflows without taking into account the temporal dimension of activities involved. Workflow also has a strong temporal aspect: activity sequencing, deadlines, routing conditions, and scheduling all involve the element of time. Once the temporal aspects of the activities in a process description are taken into account, various types of synchronization anomalies and other undesirable behavior may be detected. In this article, we address both the quantitative temporal aspect and the qualitative temporal aspect of workflow systems. On one hand, based on time Petri net (TPN), we introduce a set of linear inference algorithms to solve some quantitative inference problems of temporal constraints of workflows. On the other hand, based on extended interval temporal logic (EITL), we propose temporal logic workflow models to qualitatively infer temporal relations among tasks of workflow systems. (C) 2004 Wiley Periodicals, Inc.

关键词： Workflow Aspects(Temporal) inference algorithms Petri nets qualitative analysis Temporal logic

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Taming Serverless Cold Start of Cloud Model inference With Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第8期23卷 8111-8128页

作者： Zhao, Kongyange Zhou, Zhi Jiao, Lei Cai, Shen Xu, Fei Chen, Xu Sun Yat Sen Univ SYSU Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Oregon Dept Comp & Informat Sci Eugene OR 97403 USA East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Sch Comp Sci & Technol Shanghai 200062 Peoples R China

Serverless computing is envisioned as the de-facto standard for next-generation cloud computing. However, the cold start dilemma has impeded its adoption by delay-sensitive and burst applications. In this paper, we propose to tame serverless cold start in a cloud inference system with edge computing. Specifically, the proposed solution smooths the serverless cloud workload with user-owned edge computing, reducing the number of cold starts. Leveraging the configurability of requests and serverless functions, the proposed solution further reduces the transmission latency and serverless cost by adapting request configuration (e.g., image resolution) and function configuration (e.g., memory). To alleviate the potential inference accuracy degradation incurred by configuration adaption, we aim to strike a nice balance between inference latency, cost, and accuracy. However, achieving this goal is non-trivial since the underlying optimization is non-convex and involves future uncertain information. To simultaneously address dual challenges, the presented cold-start-aware online algorithms apply the regularization technique to decompose the problem into separate convex subproblems. Then, it applies lazy switching to smooth the number of provisioned functions and thus reduces the cold start. Through rigorous theoretical analysis, realistic prototype evaluations on AWS Lambda, and trace-driven simulations, we comprehensively validate the theoretical and empirical performance of our proposed solution.

关键词： Computational modeling Edge computing Serverless computing inference algorithms Optimization Costs Adaptation models cold start model inference edge computing online optimization

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SecBNN: Efficient Secure inference on Binary Neural Networks

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 10273-10286页

作者： Chen, Hanxiao Li, Hongwei Hao, Meng Hu, Jia Xu, Guowen Zhang, Xilin Zhang, Tianwei Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610056 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore

This work studies secure inference on Binary Neural Networks (BNNs), which have binary weights and activations as a desirable feature. Although previous works have developed secure methodologies for BNNs, they still have performance limitations and significant gaps in efficiency when applied in practice. We present SecBNN, an efficient secure two-party inference framework on BNNs. SecBNN exploits appropriate underlying primitives and contributes efficient protocols for the non-linear and linear layers of BNNs. Specifically, for non-linear layers, we introduce a secure sign protocol with an innovative adder logic and customized evaluation algorithms. For linear layers, we propose a new binary matrix multiplication protocol, where a divide-and-conquer strategy is provided to recursively break down the matrix multiplication problem into multiple sub-problems. Building on top of these efficient ingredients, we implement and evaluate SecBNN over two real-world datasets and various model architectures under LAN and WAN. Experimental results show that SecBNN substantially improves the communication and computation performance of existing secure BNN inference works by up to 29 x and 14 x, respectively.

关键词： Wide area networks Protocols Accuracy Quantization (signal) Neural networks Buildings Computer architecture inference algorithms Logic Adders Binary neural networks private inference secure two-party computation

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Data-Driven Boolean Network inference Using a Genetic Algorithm With Marker-Based Encoding

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第3期19卷 1558-1569页

作者： Liu, Xiang Shi, Ning Wang, Yan Ji, Zhicheng He, Shan Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

The inference of Boolean networks is crucial for analyzing the topology and dynamics of gene regulatory networks. Many data-driven approaches using evolutionary algorithms have been proposed based on time-series data. However, the ability to infer both network topology and dynamics is restricted by their inflexible encoding schemes. To address this problem, we propose a novel Boolean network inference algorithm for inferring both network topology and dynamics simultaneously. The main idea is that, we use a marker-based genetic algorithm to encode both regulatory nodes and logical operators in a chromosome. By using the markers and introducing more logical operators, the proposed algorithm can infer more diverse candidate Boolean functions. The proposed algorithm is applied to five networks, including two artificial Boolean networks and three real-world gene regulatory networks. Compared with other algorithms, the experimental results demonstrate that our proposed algorithm infers more accurate topology and dynamics.

关键词： Heuristic algorithms Boolean functions Biological cells Network topology Encoding inference algorithms Topology Boolean network inference data driven evolutionary algorithm marker-based encoding gene regulatory network

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