Distributed Mechanism Design for Multicast Transmission

Heydaribeni, Nasimeh; Anastasopoulos, Achilleas

doi:10.1109/cdc.2018.8618979

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…Adopting ALLC to provide speed up in performing computational tasks involved in a wide range of applications such as decentralized control, distributed optimization, data mining, etc. [41]- [45] is another future direction. Generalizing ALCC in order to evaluate multiple polynomials in one round by applying techniques utilized in multi-user secret sharing [46] is another approach to be considered for future work.…”

Section: Discussionmentioning

confidence: 99%

“…Proof: Note that in (38) the term LT N can be regarded as the noise vector and, consequently, (38) can be turned into an equation similar to (43) describing the MIMO channel model. Hence, by using this observation together with (44) and the definition of capacity, (42) leads to (45).…”

Section: A Privacy Analysis With Gaussian Noisementioning

confidence: 99%

“…Next, the provided upper bounds on the maximum amount of information revealed about the dataset to a subset T of colluding parties with size t are numerically evaluated. This is done for both the MIS security metric, bounded in (45), as well as the DS metric, bound in (49), for a certain set of parameters and the results are shown in Figure 3. Both η s and η c are plotted versus β.…”

Section: A Privacy Analysis With Gaussian Noisementioning

confidence: 99%

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Analog Lagrange Coded Computing

Soleymani¹,

Mahdavifar²,

Avestimehr³

2020

Preprint

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A distributed computing scenario is considered, where the computational power of a set of worker nodes is used to perform a certain computation task over a dataset that is dispersed among the workers. Lagrange coded computing (LCC), proposed by Yu et al., leverages the well-known Lagrange polynomial to perform polynomial evaluation of the dataset in such a scenario in an efficient parallel fashion while keeping the privacy of data amidst possible collusion of workers. This solution relies on quantizing the data into a finite field, so that Shamir's secret sharing, as one of its main building blocks, can be employed. Such a solution, however, is not properly scalable with the size of dataset, mainly due to computation overflows. To address such a critical issue, we propose a novel extension of LCC to the analog domain, referred to as analog LCC (ALCC). All the operations in the proposed ALCC protocol are done over the infinite fields of R/C but for practical implementations floating-point numbers are used. We characterize the privacy of data in ALCC, against any subset of colluding workers up to a certain size, in terms of the distinguishing security (DS) and the mutual information security (MIS) metrics. Also, the accuracy of outcome is characterized in a practical setting assuming operations are performed using floating-point numbers. Consequently, a fundamental trade-off between the accuracy of the outcome of ALCC and its privacy level is observed and is numerically evaluated. Moreover, we implement the proposed scheme to perform matrix-matrix multiplication over a batch of matrices. It is observed that ALCC is superior compared to the state-of-the-art LCC, implemented using fixed-point numbers, assuming both schemes use an equal number of bits to represent data symbols.

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Section: Discussionmentioning

confidence: 99%

Section: A Privacy Analysis With Gaussian Noisementioning

confidence: 99%

Section: A Privacy Analysis With Gaussian Noisementioning

confidence: 99%

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Analog Lagrange Coded Computing

Soleymani¹,

Mahdavifar²,

Avestimehr³

2020

Preprint

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“…The design of market mechanisms to efficiently allocate resources under incomplete information on agents' preferences and costs has received considerable attention in the operations research, economics, and computer science communities. For instance, mechanism design has enabled designing optimal resource allocation strategies even in settings when certain information is privately known to agents [12,13,14,15]. Furthermore, inverse game theory [16] and revealed preference based approaches [17,18,19,20] have emerged as methods to learn the underlying utilities and costs of agents given past observations of their actions.…”

Section: Literature Reviewmentioning

confidence: 99%

Online Learning for Equilibrium Pricing in Markets under Incomplete Information

Jalota¹,

Sun²,

Azizan³

2023

Preprint

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The study of market equilibria is central to economic theory, particularly in efficiently allocating scarce resources. However, the computation of equilibrium prices at which the supply of goods matches their demand typically relies on having access to complete information on private attributes of agents, e.g., suppliers' cost functions, which are often unavailable in practice. Motivated by this practical consideration, we consider the problem of setting equilibrium prices in the incomplete information setting wherein a market operator seeks to satisfy the customer demand for a commodity by purchasing the required amount from competing suppliers with privately known cost functions unknown to the market operator. In this incomplete information setting, we consider the online learning problem of learning equilibrium prices over time while jointly optimizing three performance metrics-unmet demand, cost regret, and payment regret-pertinent in the context of equilibrium pricing over a horizon of T periods. We first consider the setting when suppliers' cost functions are fixed and develop algorithms that achieve a regret of O(log log T ) when the customer demand is constant over time, or O( √ T log log T ) when the demand is variable over time. Next, we consider the setting when the suppliers' cost functions can vary over time and illustrate that no online algorithm can achieve sublinear regret on all three metrics when the market operator has no information about how the cost functions change over time. Thus, we consider an augmented setting wherein the operator has access to hints/contexts that, without revealing the complete specification of the cost functions, reflect the variation in the cost functions over time and propose an algorithm with sublinear regret in this augmented setting.

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“…As a result, there has been a growing interest in designing mechanisms that do not require complete information of users' preferences to achieve an efficient resource allocation. To this end, mechanism design has enabled the truthful elicitation of users' private information [22,23,24]. Furthermore, inverse game theory [25] has enabled the learning of users' preferences, e.g., O-D travel demands [26,27]in traffic routing.…”

Section: Literature Reviewmentioning

confidence: 99%

Online Learning for Traffic Routing under Unknown Preferences

Jalota¹,

Gopalakrishnan²,

Azizan³

et al. 2022

Preprint

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In transportation networks, users typically choose routes in a decentralized and self-interested manner to minimize their individual travel costs, which, in practice, often results in inefficient overall outcomes for society. As a result, there has been a growing interest in designing road tolling schemes to cope with these efficiency losses and steer users toward a system-efficient traffic pattern. However, the efficacy of road tolling schemes often relies on having access to complete information on users' trip attributes, such as their origin-destination (O-D) travel information and their values of time, which may not be available in practice.Motivated by this practical consideration, we propose an online learning approach to set tolls in a traffic network to drive heterogeneous users with different values of time toward a system-efficient traffic pattern. In particular, we develop a simple yet effective algorithm that adjusts tolls at each time period solely based on the observed aggregate flows on the roads of the network without relying on any additional trip attributes of users, thereby preserving user privacy. In the setting where the O-D pairs and values of time of users are drawn i.i.d. at each period, we show that our approach obtains an expected regret and road capacity violation of O( √ T ), where T is the number of periods over which tolls are updated. Our regret guarantee is relative to an offline oracle that has complete information on users' trip attributes. We further establish a Ω( √ T ) lower bound on the regret of any algorithm, which establishes that our algorithm is optimal up to constants. Finally, we demonstrate the superior performance of our approach relative to several benchmarks on a real-world transportation network, thereby highlighting its practical applicability.

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Distributed Mechanism Design for Multicast Transmission

Cited by 10 publications

References 14 publications

Analog Lagrange Coded Computing

Analog Lagrange Coded Computing

Online Learning for Equilibrium Pricing in Markets under Incomplete Information

Online Learning for Traffic Routing under Unknown Preferences

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