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IEEE Information Theory Workshop (ITW)

作者： Faour, Sara Hamad, Mustapha Sarkiss, Mireille Wigger, Michele IP Paris Telecom Paris LTCI Palaiseau France Paris Res Ctr Huawei Math & Algorithm Sci Lab Paris France IP Paris Telecom SudParis SAMOVAR Palaiseau France

ISBN: (纸本)9798350301496

We study a distributed binary hypothesis testing (HT) problem with communication and security constraints, involving three parties: a remote sensor called Alice, a legitimate decision center called Bob, and an eavesdropper called Eve, all having their own source observations. In this system, Alice conveys a rate-R description of her observations to Bob, and Bob performs a binary hypothesis test on the joint distribution underlying his and Alice's observations. The goal of Alice and Bob is to maximize the exponential decay of Bob's miss-detection (type-II error) probability under two constraints: Bob's falsealarm (type-I error) probability has to stay below a given threshold and Eve's uncertainty (equivocation) about Alice's observations should stay above a given security threshold even when Eve learns Alice's message. For the special case of testing against independence, we characterize the largest possible type-II error exponent under the described type-I error probability and security constraints.

关键词： distributed hypothesis testing error exponents security constraints side information

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Communication-Efficient Distribution-Free Inference Over Networks 57

Communication-Efficient Distribution-Free Inference Over Net...

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57th Asilomar Conference on Signals, Systems and Computers

作者： Pournaderi, Mehrdad Xiang, Yu Univ Utah Dept Elect & Comp Engn 50 Cent Campus Dr 2110 Salt Lake City UT 84112 USA

ISBN: (纸本)9798350325744

Consider a star network where each local node possesses a set of test statistics that exhibit a symmetric distribution around zero when their corresponding null hypothesis is true. This paper investigates statistical inference problems in networks concerning the aggregation of this general type of statistics and global error rate control under communication constraints in various scenarios. The study proposes communication-efficient algorithms that are built on established non-parametric methods, such as the Wilcoxon and sign tests, as well as modern inference methods such as the Benjamini-Hochberg (BH) and Barber-Candes (BC) procedures, coupled with sampling and quantization operations. The proposed methods are evaluated through extensive simulation studies.

关键词： distributed hypothesis testing FDR control distribution-free methods communication efficiency

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On distributed Learning With Constant Communication Bits

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2022年第1期3卷 125-134页

作者： Xu, Xiangxiang Huang, Shao-Lun Tsinghua Berkeley Shenzhen Inst Data Sci & Informat Technol Res Ctr Shenzhen 518055 Peoples R China

In this paper, we study a distributed learning problem constrained by constant communication bits. Specifically, we consider the distributed hypothesis testing (DHT) problem where two distributed nodes are constrained to transmit a constant number of bits to a central decoder. In such settings, we show that in order to achieve the optimal error exponents, it suffices to consider the empirical distributions of observed data sequences and encode them to the transmission bits. With such a coding strategy, we develop a geometric approach in the distribution spaces and establish an inner bound of error exponent regions. In particular, we show the optimal achievable error exponents and coding schemes for the following cases: (i) both nodes can transmit log(2)(3) bits;(ii) one of the nodes can transmit 1 bit, and the other node is not constrained;(iii) the joint distribution of the nodes are conditionally independent given one hypothesis. Furthermore, we provide several numerical examples for illustrating the theoretical results. Our results provide theoretical guidance for designing practical distributed learning rules, and the developed approach also reveals new potentials for establishing error exponents for DHT with more general communication constraints.

关键词： distributed learning distributed hypothesis testing communication constraints multiterminal data compression error exponent statistical inference

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HIDDEN MARKOV MODELING OVER GRAPHS

HIDDEN MARKOV MODELING OVER GRAPHS

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IEEE Data Science and Learning Workshop (DSLW)

作者： Kayaalp, Mert Bordignon, Virginia Vlaski, Stefan Sayed, Ali H. Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland Imperial Coll London London England

ISBN: (纸本)9781665454261

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference from the optimal centralized Bayesian solution is asymptotically bounded for geometrically ergodic transition models. Experiments illustrate the theoretical findings and in particular, demonstrate the superior performance of the proposed algorithm compared to a state-of-the-art social learning algorithm.

关键词： Hidden Markov models distributed hypothesis testing social learning sequential state estimation

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distributed Inference With Sparse and Quantized Communication

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 3906-3921页

作者： Mitra, Aritra Richards, John Bagchi, Saurabh Sundaram, Shreyas Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Sandia Natl Labs Albuquerque NM 87123 USA

We consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. We focus on scenarios where communication between agents is costly, and takes place over channels with finite bandwidth. To reduce the frequency of communication, we develop a novel event-triggered distributed learning rule that is based on the principle of diffusing low beliefs on each false hypothesis. Building on this principle, we design a trigger condition under which an agent broadcasts only those components of its belief vector that have adequate innovation, to only those neighbors that require such information. We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents. Next, to deal with finite-precision communication channels, we propose a distributed learning rule that leverages the idea of adaptive quantization. We show that by sequentially refining the range of the quantizers, every agent can learn the truth exponentially fast almost surely, while using just 1 bit to encode its belief on each hypothesis. For both our proposed algorithms, we rigorously characterize the trade-offs between communication-efficiency and the learning rate.

关键词： Laboratories Distance learning Convergence Computer aided instruction Quantization (signal) testing Technological innovation distributed inference bayesian learning distributed hypothesis testing communication-efficiency

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Strong Converse Bounds in Quantum Network Information Theory

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第4期67卷 2269-2292页

作者： Cheng, Hao-Chung Datta, Nilanjana Rouze, Cambyse Natl Taiwan Univ Dept Elect Engn Grad Inst Commun Engn Taipei 10617 Taiwan Natl Taiwan Univ Dept Math Taipei 10617 Taiwan Natl Taiwan Univ Inst Appl Math Sci Taipei 10617 Taiwan Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB2 1TN England Tech Univ Munich Dept Math D-80333 Mtinchen Germany

In this paper, we develop the first method for finding strong converse bounds in quantum network information theory. The general scheme relies on a recently obtained result in the field of non-commutative functional inequalities, namely the tensorization property of quantum reverse hypercontractivity for the quantum depolarizing semigroup. We develop a novel technique to employ this result to find both finite blocklength and exponential strong converse bounds for the tasks of quantum source coding with compressed classical side information, and distributed quantum hypothesis testing with communication constraints for a classical-quantum state. In the classical setting, these two problems can be reformulated in a unified framework in terms of the so-called image-size characterization problem, which we extend to the classical-quantum setting. We also use this technique to establish analogous strong converse bounds in broadcast communication scenarios. In particular, we consider the transmission of classical information through a degraded broadcast channel, whose outputs are two quantum systems, with the state of one being a degraded version of the other. In establishing this last result, we prove a second-order Fano-type inequality, which is of independent interest. Our method to study strong converses has potential applications in other important tasks of quantum network information theory.

关键词： Task analysis testing Information theory Source coding Entropy Quantum networks Error probability Strong converse quantum network information theory quantum reverse hypercontractivity degraded broadcast channel (DBC) distributed hypothesis testing

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Cooperative Multi-Sensor Detection under Variable-Length Coding

Cooperative Multi-Sensor Detection under Variable-Length Cod...

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IEEE Information Theory Workshop (ITW)

作者： Hamad, Mustapha Wigger, Michele Sarkiss, Mireille Telecom Paris IP Paris LTCI F-91120 Palaiseau France Telecom SudParis IP Paris SAMOVAR F-91011 Evry France

ISBN: (纸本)9781728159621

We investigate the testing-against-independence problem over a cooperative MAC with two sensors and a single detector under an average rate constraint on the sensors-detector links. For this setup, we design a variable-length coding scheme that maximizes the achievable type-II error exponent when the type-I error probability is limited to epsilon. Similarly to the single-link result, we show here that the optimal error exponent depends on epsilon and that variable-length coding allows to increase the rates over the optimal fixed-length coding scheme by the factor (1 - epsilon)(-1).

关键词： distributed hypothesis testing Cooperative MAC Variable-Length Coding Error Exponent

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Two-Hop Network with Multiple Decision Centers under Expected-Rate Constraints

Two-Hop Network with Multiple Decision Centers under Expecte...

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IEEE Global Communications Conference (GLOBECOM)

作者： Hamad, Mustapha Wigger, Michele Sarkiss, Mireille IP Paris Telecom Paris LTCI F-91120 Palaiseau France IP Paris Telecom SudParis SAMOVAR F-91011 Evry France

ISBN: (纸本)9781728181042

The paper studies distributed binary hypothesis testing over a two-hop relay network where both the relay and the receiver decide on the hypothesis. Both communication links are subject to expected rate constraints, which differs from the classical assumption of maximum rate constraints. We exactly characterize the set of type-II error exponent pairs at the relay and the receiver when both type-I error probabilities are constrained by the same value epsilon > 0. No tradeoff is observed between the two exponents, i.e., one can simultaneously attain maximum type-II error exponents both at the relay and at the receiver. For epsilon(1) not equal epsilon(2), we present an achievable exponents region, which we obtain with a scheme that applies different versions of a basic two-hop scheme that is optimal under maximum rate constraints. We use the basic twohop scheme with two choices of parameters and rates, depending on the transmitter's observed sequence. For epsilon(1) = epsilon(2), a single choice is shown to be sufficient. Numerical simulations indicate that extending to three or more parameter choices is never beneficial.

关键词： Multi-hop distributed hypothesis testing error exponents expected rate constraints variable-length coding

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Optimal Exponents in Cascaded hypothesis testing under Expected Rate Constraints

Optimal Exponents in Cascaded Hypothesis Testing under Expec...

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IEEE Information Theory Workshop (ITW)

作者： Hamad, Mustapha Wigger, Michele Sarkiss, Mireille Telecom Paris IP Paris LTCI F-91120 Palaiseau France Telecom SudParis IP Paris SAMOVAR F-91011 Evry France

ISBN: (纸本)9781665403122

Cascaded binary hypothesis testing is studied in this paper with two decision centers at the relay and the receiver. All terminals have their own observations, where we assume that the observations at the transmitter, the relay, and the receiver form a Markov chain in this order. The communication occurs over two hops, from the transmitter to the relay, and from the relay to the receiver. Expected rate constraints are imposed on both communication links. In this work, we characterize the optimal type-II error exponents at the two decision centers under constraints on the allowed type-I error probabilities. Our recent work characterized the optimal type-II error exponents in the special case when the two decision centers have same type-I error constraints and provided an achievability scheme for the general setup. To obtain the exact characterization for the general case, in this paper we provide a new converse proof as well as a new matching achievability scheme. Our results indicate that under unequal type-I error constraints at the relay and the receiver, a tradeoff arises between the maximum type-II error probabilities at these two terminals. Previous results showed that such a tradeoff does not exist under equal type-I error constraints or under general type-I error constraints when a maximum rate constraint is imposed on the communication links.

关键词： Multi-hop distributed hypothesis testing error exponents expected rate constraints variable-length coding

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distributed Learning Over Networks: Effect of Using Historical Observations

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第12期65卷 5503-5509页

作者： Shi, Chong-Xiao Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Minist Educ Key Lab Integrated Automat Proc Ind Shenyang 110819 Peoples R China

This article investigates the problem of distributed hypothesis testing over networks. By introducing double and triple cumulations of historical observations, two novel distributed learning algorithms are developed to solve the considered problem. Within this framework, we study the convergence rates of the proposed algorithms. More specifically: 1) It is proven that the algorithm with double cumulation of historical observations has a faster convergence rate than those of the existing algorithms. 2) Furthermore, it is proven that the algorithm with triple cumulation of historical observations can be further accelerated. Especially, based on the theoretical results, this article shows that a tradeoff among convergence rate, transient performance, and memory burden exists in the proposed algorithms. Finally, simulation examples verify the theoretical results.

关键词： Convergence testing Bayes methods Silicon Acceleration Transient analysis Knowledge engineering Convergence rate distributed hypothesis testing historical observations non-Bayesian learning

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