A game-theoretic perspective on communication for omniscience

Ding, Ni; Chan, Chung; Tie, Liu; Kennedy, Rodney A.; Sadeghi, Parastoo

doi:10.1109/ausctw.2016.7433656

Cited by 6 publications

(17 citation statements)

References 23 publications

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“…A different coalition-game model for the CDE problem was recently proposed in [12]. This model, however, differs from our work in two aspects: (i) the utility function under the consideration is different from ours, and (ii) the criteria for the stability of the grand coalition is different from the Pareto optimality being considered here.…”

Section: A Related Workmentioning

confidence: 82%

A Monetary Mechanism for Stabilizing Cooperative Data Exchange with Selfish Users

Heidarzadeh

Tyagi

Shakkottai

et al. 2018

2018 IEEE International Symposium on Information Theory (ISIT)

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This paper considers the problem of stabilizing cooperative data exchange with selfish users. In this setting, each user has a subset of packets in the ground set X, and wants all other packets in X. The users can exchange their packets by broadcasting coded or uncoded packets over a lossless broadcast channel, and monetary transactions are allowed between any pair of users. We define the utility of each user as the sum of two sub-utility functions: (i) the difference between the total payment received by the user and the total transmission rate of the user, and (ii) the difference between the total number of required packets by the user and the total payment made by the user. A rate-vector and payment-matrix pair (r, p) is said to stabilize the grand coalition (i.e., the set of all users) if (r, p) is Pareto optimal over all minor coalitions (i.e., all proper subsets of users who collectively know all packets in X). Our goal is to design a stabilizing rate-payment pair with minimum total sum-rate and minimum total sum-payment for any given instance of the problem. In this work, we propose two algorithms that find such a solution. Moreover, we show that both algorithms maximize the sum of utility of all users (over all solutions), and one of the algorithms also maximizes the minimum utility among all users (over all solutions).

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Section: A Related Workmentioning

confidence: 82%

A Monetary Mechanism for Stabilizing Cooperative Data Exchange with Selfish Users

Heidarzadeh

Tyagi

Shakkottai

et al. 2018

2018 IEEE International Symposium on Information Theory (ISIT)

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“…3) The egalitarian solution [19] aims to equalize the rate/cost allocation in the optimal rate region regardless of the marginal 3 The fundamental partition P * is an optimizer that determines the minimum sum-rate R * [9]. See also Section II-A.…”

Section: A Summary Of Main Resultsmentioning

confidence: 99%

“…They are also very grateful to Prof. Chung Chan, Mr. Qiaoqiao Zhou and Prof. Tie Liu for their early contributions to the coalitional game model of CO and the decomposition property of the egalitarian solution in [1], [3], as well as the insightful discussions about the interpretation of the Shapley value. The authors would like to thank Dr. Ali Al-Bashabsheh for his comments on the egalitarian solution in Section V of this paper.…”

Section: Acknowledgmentmentioning

confidence: 99%

Fairness in communication for omniscience

Ding

Chan

Zhou

et al. 2016

2016 IEEE International Symposium on Information Theory (ISIT)

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Abstract-We consider the problem of how to fairly distribute the minimum sum-rate among the users in communication for omniscience (CO). We formulate a problem of minimizing a weighted quadratic function over a submodular base polyhedron which contains all achievable rate vectors, or transmission strategies, for CO that have the same sum-rate. By solving it, we can determine the rate vector that optimizes the Jain's fairness measure, a more commonly used fairness index than the Shapley value in communications engineering. We show that the optimizer is a lexicographically optimal (lex-optimal) base and can be determined by a decomposition algorithm (DA) that is based on submodular function minimization (SFM) algorithm and completes in strongly polynomial time. We prove that the lex-optimal minimum sum-rate strategy for CO can be determined by finding the lex-optimal base in each user subset in the fundamental partition and the complexity can be reduced accordingly.I. INTRODUCTION Communication for omniscience (CO) is a problem proposed in [1]. It is assumed that there is a group of users in the system and each of them observes a component of a discrete memoryless multiple source in private. The users can exchange their information in order to attain omniscience, the state that each user obtains the total information in the entire multiple source in the system. A typical example is the coded cooperative data exchange (CCDE) problem [2] where a group of geographically close users communicate with each other via error-free broadcast channels in order to recover a packet set.The fundamental problem in CO and CCDE is how to achieve omniscience with minimum total transmission rate, or minimum sum-rate. The studies in [3], [4] show that the minimum sum-rate can be obtained by solving an optimization problem over the partitions of user set, while the authors in [5], [6] propose polynomial time algorithms that determine the minimum sum-rate and a corresponding strategy based on submodular function minimization (SFM) techniques. However, the minimum sum-rate strategy is not unique in general, and the algorithms in [5], [6] utilizing Edmond's greedy algorithm [7] necessarily return an extremal point, or an unfair minimum sum-rate strategy.On the other hand, fairness is an important factor in CO. For example, in CCDE, the users are considered as peers in wireless communications. A fair transmission strategy encourages them to take part in CO and helps prevent driving the

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“…Steps 7 and 8 in the SPLIT algorithm indicate an adaptive rate update method: In step 7, since r * C\X ≥ f (X) w(X) w C\X , 10 we first assign f (X) w(X) w C\X to r * C\X and determine the remaining rates by calling SPLIT(C \X, g, w C\X ) in step 8. It is easy to see that r * V ∈ P (H, ≤) after each execution of step 7.…”

Section: Adaptive and Distributed Implementationmentioning

confidence: 99%

“…For a system where the terminals (also known as users or nodes) are equally privileged, e.g., a WSN, we always seek to attain fairness in the SW region. Several game-theoretic contributions in [8][9][10] showed that the Shapley value [11] is one solution within the SW region. However, computing the Shapley value is intractable in large scale systems due to the exponentially growing complexity in the number of terminals.…”

Section: Introductionmentioning

confidence: 99%

Fairness in Multiterminal Data Compression: A Splitting Method for the Egalitarian Solution

Ding

Smith

Sadeghi

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes a novel splitting (SPLIT) algorithm to achieve fairness in the multiterminal lossless data compression problem. It finds the egalitarian solution in the Slepian-Wolf region and completes in strongly polynomial time. We show that the SPLIT algorithm adaptively updates the source coding rates to the optimal solution, while recursively splitting the terminal set, enabling parallel and distributed computation. The result of an experiment demonstrates a significant reduction in computation time by the parallel implementation when the number of terminals becomes large. The achieved egalitarian solution is also shown to be superior to the Shapley value in distributed networks, e.g., wireless sensor networks, in that it best balances the nodes' energy consumption and is far less computationally complex to obtain.

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A game-theoretic perspective on communication for omniscience

Cited by 6 publications

References 23 publications

A Monetary Mechanism for Stabilizing Cooperative Data Exchange with Selfish Users

A Monetary Mechanism for Stabilizing Cooperative Data Exchange with Selfish Users

Fairness in communication for omniscience

Fairness in Multiterminal Data Compression: A Splitting Method for the Egalitarian Solution

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