Decentralized clustering over adaptive networks

Khawatmi, Sahar; Zoubir, Abdelhak M.; Sayed, Ali H.

doi:10.1109/eusipco.2015.7362874

Cited by 20 publications

(24 citation statements)

References 13 publications

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“…This is an effective measure and has been applied to multi-task networks and distributed clustering problems [5]. Several variants focusing on adaptive weights applied to multi-task networks can be found in [35], [36], [18], [37]. Note that the essence of adaptive weights is similar to distributed detection.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, it turns the detection method from a binary classification problem to a regression problem. Detection approach has also been applied in [35] for clustering over diffusion networks. Although adaptive weights provide some degree of resilience to byzantine adversaries with fixed values, we have shown in this work that adaptive weights may introduce vulnerabilities that allow time-dependent deception attacks.…”

Section: Related Workmentioning

confidence: 99%

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Resilient Distributed Diffusion in Networks With Adversaries

Abbas

Koutsoukos

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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In this paper, we study resilient distributed diffusion for multi-task estimation in the presence of adversaries where networked agents must estimate distinct but correlated states of interest by processing streaming data. We show that in general diffusion strategies are not resilient to malicious agents that do not adhere to the diffusion-based information processing rules. In particular, by exploiting the adaptive weights used for diffusing information, we develop time-dependent attack models that drive normal agents to converge to states selected by the attacker. We show that an attacker that has complete knowledge of the system can always drive its targeted agents to its desired estimates. Moreover, an attacker that does not have complete knowledge of the system including streaming data of targeted agents or the parameters they use in diffusion algorithms, can still be successful in deploying an attack by approximating the needed information. The attack models can be used for both stationary and non-stationary state estimation. In addition, we present and analyze a resilient distributed diffusion algorithm that is resilient to any data falsification attack in which the number of compromised agents in the local neighborhood of a normal agent is bounded. The proposed algorithm guarantees that all normal agents converge to their true target states if appropriate parameters are selected. We also analyze trade-off between the resilience of distributed diffusion and its performance in terms of steady-state mean-square-deviation (MSD) from the correct estimates. Finally, we evaluate the proposed attack models and resilient distributed diffusion algorithm using stationary and nonstationary multi-target localization.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Resilient Distributed Diffusion in Networks With Adversaries

Abbas

Koutsoukos

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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“…[9][10][11][12][13][14][15][16][17] Solving cooperative learning problems that include multiple objectives is challenging because cooperation between agents with different objectives may lead to disastrous results. Nevertheless, there are important situations where agents in the network are interested in multiple objectives that are different from each other.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, there are important situations where agents in the network are interested in multiple objectives that are different from each other. [9][10][11][12][13][14][15][16][17] Solving cooperative learning problems that include multiple objectives is challenging because cooperation between agents with different objectives may lead to disastrous results. 16,17 One useful way to extract similarities among objectives is to formulate optimization problems based on information theoretic learning cost functions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multitask network based on maximum correntropy learning algorithm

Hajiabadi

Hodtani

Khoshbin

2017

Adaptive Control & Signal

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Adaptive networks solve distributed optimization problems in which all agents of the network are interested to collaborate with their neighbors to learn a similar task. Collaboration is useful when all agents seek a similar task. However, in many applications, agents may belong to different clusters that seek dissimilar tasks. In this case, nonselective collaboration will lead to damaging results that are worse than noncooperative solution. In this paper, we contribute in problems that several clusters of interconnected agents are interested in learning multiple tasks. To address multitask learning problem, we consider an information theoretic criterion called correntropy in a distributed manner providing a novel adaptive combination policy that allows agents to learn which neighbors they should cooperate with and which other neighbors they should reject. In doing so, the proposed algorithm enables agents to recognize their clusters and to achieve improved learning performance compared with noncooperative strategy. Stability analysis in the mean sense and also a closed-form relation determining the network error performance in steady-state mean-square-deviation is derived. Simulation results illustrate the theoretical findings and match well with theory. KEYWORDSadaptive network, correntropy criterion, distributed processing, multitask learning 1 1232

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“…In particular, object or speaker labeling can be solved by in-network adaptive classification algorithms where a minimum amount of information is exchanged among single-hop neighbors. Various methods have been proposed that deal with distributed data clustering and classification, e.g., [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. In the last few years, several distributed adaptive strategies, such as incremental, consensus, and diffusion least mean squares algorithms have been developed [25].…”

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confidence: 99%

Robust and adaptive diffusion-based classification in distributed networks

Binder

Muma

Zoubir

2016

EURASIP J. Adv. Signal Process.

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Distributed adaptive signal processing and communication networking are rapidly advancing research areas which enable new and powerful signal processing tasks, e.g., distributed speech enhancement in adverse environments. An emerging new paradigm is that of multiple devices cooperating in multiple tasks (MDMT). This is different from the classical wireless sensor network (WSN) setup, in which multiple devices perform one single joint task. A crucial first step in order to achieve a benefit, e.g., a better node-specific audio signal enhancement, is the common unique labeling of all relevant sources that are observed by the network. This challenging research question can be addressed by designing adaptive data clustering and classification rules based on a set of noisy unlabeled sensor observations. In this paper, two robust and adaptive distributed hybrid classification algorithms are introduced. They consist of a local clustering phase that uses a small part of the data with a subsequent, fully distributed on-line classification phase. The classification is performed by means of distance-based similarity measures. In order to deal with the presence of outliers, the distances are estimated robustly. An extensive simulation-based performance analysis is provided for the proposed algorithms. The distributed hybrid classification approaches are compared to a benchmark algorithm where the error rates are evaluated in dependence of different WSN parameters. Communication cost and computation time are compared for all algorithms under test. Since both proposed approaches use robust estimators, they are, to a certain degree, insensitive to outliers. Furthermore, they are designed in a way that they are applicable to on-line classification problems.

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Decentralized clustering over adaptive networks

Cited by 20 publications

References 13 publications

Resilient Distributed Diffusion in Networks With Adversaries

Resilient Distributed Diffusion in Networks With Adversaries

Adaptive multitask network based on maximum correntropy learning algorithm

Robust and adaptive diffusion-based classification in distributed networks

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