Session Regular-7

Social Networks, Pricing, Economics

9:00 AM — 10:30 AM EDT
Oct 21 Thu, 9:00 AM — 10:30 AM EDT

Personalized Pricing through User Profiling in Social Networks

Qinqi Lin (The Chinese University of Hong Kong, Shenzhen, China), Lingjie Duan (Singapore University of Technology and Design, Singapore) and Jianwei Huang (The Chinese University of Hong Kong, Shenzhen, China)

User profiling allows product sellers to identify users' willingness to pay and enable personalized pricing. However, users' information exploited in profiling is usually private and hard to obtain accurately due to users' privacy concerns. With the increasing popularity of social networks, where users reveal their private information through social interactions, more sellers today profile users through their social data. This paper is the first to study how a seller optimizes personalized pricing through user profiling on social networks, where users proactively react by controlling their social activities and information leakage. We formulate and analyze a dynamic Bayesian game played between users and the seller. First, users decide their social activities by trading off the social network benefit against the potential risk of revealing private information. Then, the seller exploits users' profiles to determine the personalized prices for the profiled users and a uniform price for the non-profiled users. It is challenging to analyze the Perfect Bayesian Equilibrium (PBE) of this game due to i) the randomness in user profiling, and ii) the coupling among users' activity levels and that between the seller's pricing decisions and users' social activities. Despite the difficulty, we propose to alternate backward induction and forward induction to successfully solve the PBE. We show the surprising result that users' activity levels do not monotonically decrease as the profiling technology improves. Instead, when user profiling is of high accuracy, the seller strategically chooses a high uniform price to stimulate their increased social activities to profile more users.

Social Influencer Selection by Budgeted Portfolio Optimization

Ricardo José López Dawn and Anastasios Giovanidis (Sorbonne Université, CNRS-LIP6, France)

Influencer marketing has become in the recent years a thriving industry that includes more than 1120 agencies worldwide and with a global market value expected to reach 15 billion dollars by 2022. The advertising problem that such agencies face is the following: given a monetary budget find a set of appropriate influencers on a social platform and recruit them to create a number of posts for the promotion of a certain product. The objective of the campaign is to maximize some impact metric, e.g. the number of impressions, the sales, or the audience reach. In this work, we present an original formulation of the budgeted campaign orchestration problem as a convex program, and further derive a near-optimal algorithm to solve it efficiently. The proposed algorithm has low computational complexity and can scale well for problems with large numbers (millions) of social users, encountered in real-world platforms. We apply our algorithm to a Twitter dataset and illustrate the optimal campaign performance for various metrics of interest.

Edgeconomics: Price Competition and Selfish Computation Offloading in Multi-Server Edge Computing Networks

Ziya Chen, Qian Ma (Sun Yat-sen University, China), Lin Gao Harbin Institute of Technology), and Xu Chen (Sun Yat-sen University, China)

As edge computing provides crucial support for delay-sensitive and computation-intensive applications, many business entities deploy their own edge servers to compete for users, which forms multi-server edge computing networks. However, no prior work studies the competition among heterogeneous edge servers and how the competition affects users' selfish computation offloading behaviors in such a network from an economic perspective. In this paper, we model the interactions between edge servers and users as a two-stage game. In Stage I, edge servers with heterogeneous marginal costs set their service prices to compete for users, and in Stage II, each user selfishly offloads its task to one of the edge servers or the remote cloud. Analyzing the equilibrium of the two-stage game is challenging due to edge servers' heterogeneity and the congestion effect caused by resource sharing among users. For users' selfish computation offloading game in Stage II and edge servers' price competition game in Stage I, we derive the explicit expression of the NE and the conditions for the uniqueness of NE. We show that at equilibrium, users only choose low-priced edge servers, and hence edge servers with low marginal costs can win the price competition, which reflects the improvement of economic efficiency in competitive markets. Moreover, it is surprising that the equilibrium prices do not monotonically increase with the task execution delay. This is because a long execution delay gives a chance to edge servers with high marginal costs to win the competition, which results in more fierce competition among edge servers.

Session Chair

Krishna Jagannathan (Indian Institute of Technology, Madras, India)

Session Break-7

Virtual Coffee Break

10:30 AM — 11:00 AM EDT
Oct 21 Thu, 10:30 AM — 11:00 AM EDT

Session Keynote-3

Keynote 3

11:00 AM — 12:00 PM EDT
Oct 21 Thu, 11:00 AM — 12:00 PM EDT

Auditing Network Traffic for Data Collection Practices

Athina Markopoulou (University of California, Irvine, USA)

Personal data collection typically starts on user devices, in a range of application domains including the web, mobile, Internet-of-Things, smart TV, and virtual reality. Many useful services are enabled by the collection of personal data, although increasingly at the expense of privacy, security, transparency, and fairness, for individuals and for the society as a whole. To deal with these problems, technical solutions are currently being developed to provide privacy guarantees and/or to expose data collection by various players in the data tracking ecosystem. Increased public awareness has also led to recent legislation on data protection, such as GDPR and CCPA, and policy has become a powerful tool to be used in synergy with technology.

In this talk, I will first describe the landscape of personal data collection and tracking today. I will then present our approach, which leverages the network as a universal vantage point for auditing the data collection practices of application developers as well as of various platforms. We develop tools for intercepting and inspecting traffic at the edge of the network, and we analyze the network traffic to look for exposures of personal data. We also check the consistency of network traffic with the corresponding privacy policies and eventually the compliance with data protection laws. I will argue that, going forward, the design of devices, software and networks needs to incorporate privacy-by-design, rather than be treated as an afterthought.

Session Chair

Eytan Modiano (Massachusetts Institute of Technology, USA)

Session Break-8

Virtual Lunch Break

12:15 PM — 2:00 PM EDT
Oct 21 Thu, 12:15 PM — 2:00 PM EDT

Session Invited-1

Invited Track I

2:00 PM — 4:00 PM EDT
Oct 21 Thu, 2:00 PM — 4:00 PM EDT

The Gittins Policy in the M/G/1 Queue

Ziv Scully and Mor Harchol-Balter (Carnegie Mellon University, USA)

The Gittins policy is a highly general scheduling policy that minimizes a wide variety of mean holding cost metrics in the M/G/1 queue. Perhaps most famously, Gittins minimizes mean response time in the M/G/1 when jobs' service times are unknown to the scheduler. Gittins also minimizes weighted versions of mean response time. For example, the well-known "cμ rule", which minimizes class-weighted mean response time in the multiclass M/M/1, is a special case of Gittins. However, despite the extensive literature on Gittins in the M/G/1, it contains no fully general proof of Gittins's optimality. This is because Gittins was originally developed for the multi-armed bandit problem. Translating arguments from the multi-armed bandit to the M/G/1 is technically demanding, so it has only been done rigorously in some special cases. The extent of Gittins's optimality in the M/G/1 is thus not entirely clear. In this work we provide the first fully general proof of Gittins's optimality in the M/G/1. The optimality result we obtain is even more general than was previously known. For example, we show that Gittins minimizes mean slowdown in the M/G/1 with unknown or partially known service times, and we show that Gittins's optimality holds under batch arrivals. Our proof uses a novel approach that works directly with the M/G/1, avoiding the difficulties of translating from the multi-armed bandit problem.

Signaling Games in Higher Dimensions: Geometric Properties of Equilibrium Partitions

Ertan Kazıklı, Sinan Gezici (Bilkent University, Turkey) and Serdar Yüksel (Queen's University, Canada)

Signaling game problems investigate communication scenarios where encoder(s) and decoder(s) have misaligned objectives due to the fact that they either employ different cost functions or have inconsistent priors. We investigate a signaling game problem where an encoder observes a multi-dimensional source and conveys a message to a decoder, and the quadratic objectives of the encoder and decoder are misaligned due to a bias vector. For the scalar case, Crawford and Sobel in their seminal paper, show that under certain technical assumptions an encoding policy must be a quantization policy at any Nash equilibrium. We first provide a set of geometry conditions that needs to be satisfied in equilibrium considering any multi-dimensional source. Then, we consider multi-dimensional sources with independent and identically distributed components and completely characterize conditions under which a Nash equilibrium with a linear encoder exists. In particular, we show that if the components of the bias vector are not equal in magnitude, then there exists a linear equilibrium if and only if the source distribution is Gaussian. On the other hand, for a linear equilibrium to exist in the case of equal bias components, it is required that the source density is symmetric around its mean. Moreover, in the case of Gaussian sources, our results have a rate-distortion theoretic implication that achievable rates and distortions in the considered game theoretic setup can be obtained from their team theoretic counterpart.

Federated Few-Shot Learning with Adversarial Learning

Chenyou Fan (Shenzhen Institute of Artificial Intelligence and Robotics for Society, China) and Jianwei Huang (The Chinese University of Hong Kong, Shenzhen, China)

We are interested in developing a unified machine learning framework for effectively training machine learning models from many small data sources such as mobile devices. This is a commonly encountered situation in mobile computing scenarios, where data is scarce and distributed while the tasks are distinct. In this paper, we propose a federated few-shot learning (FedFSL) framework to learn a few-shot classification model that can classify unseen data classes with only a few labeled samples. With the federated learning strategy, FedFSL can utilize many data sources while keeping data privacy and communication efficiency. To tackle the issue of obtaining misaligned decision boundaries produced by client models, we propose to regularize local updates by minimizing the divergence of client models. We also formulate the training in an adversarial fashion and optimize the client models to produce a discriminative feature space that can better represent unseen data samples. We demonstrate the intuitions and conduct experiments to show our approaches outperform baselines by more than 10% in learning benchmark vision tasks and 5% in language tasks.

Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT

Chen-Feng Liu and Mehdi Bennis (University of Oulu, Finland)

While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller's available information, measured by the age of information (AoI), is paramount for high-performing industrial automation. The problem in this work is cast as a sensor's transmit power minimization subject to the peak-AoI requirement and a probabilistic constraint on queuing latency. We further characterize the tail behavior of the latency by a generalized Pareto distribution (GPD) for solving the power allocation problem through Lyapunov optimization. As each sensor utilizes its own data to locally train the GPD model, we incorporate federated learning and propose a local-model selection approach which accounts for correlation among the sensor's training data. Numerical results show the tradeoff between the transmit power, peak AoI, and delay's tail distribution. Furthermore, we verify the superiority of the proposed correlation-aware approach for selecting the local models in federated learning over an existing baseline.

A Framework for Sustainable Federated Learning

Basak Guler (University of California, Riverside, USA) and Aylin Yener (The Ohio State University, USA)

Potential environmental impact of machine learning in large-scale wireless networks is a major challenge for the sustainability of next-generation intelligent systems. Federated learning is a recent framework for communication-efficient training of machine learning models over the data collected, stored, and processed by millions of wireless devices. In this paper, we introduce a sustainable machine learning framework for federated learning, using rechargeable devices that can collect energy from the ambient environment. In particular, we propose a practical federated learning framework that utilizes intermittent energy arrivals for training, with provable convergence guarantees. Our framework can be applied to both cross-device and cross-silo federated learning settings, including federated learning in wireless edge networks and the Internet-of-Things. Our experiments demonstrate that the proposed framework can provide significant performance improvement over the benchmark energy-agnostic federated learning settings.

Session Chair

Javad Ghaderi (Columbia University, USA)

Session Break-9

Virtual Coffee Break

4:00 PM — 4:30 PM EDT
Oct 21 Thu, 4:00 PM — 4:30 PM EDT

Session Invited-2

Invited Track II

4:30 PM — 6:30 PM EDT
Oct 21 Thu, 4:30 PM — 6:30 PM EDT

Eavesdropping with Intelligent Reflective Surfaces: Threats and Defense Strategies

Francesco Malandrino (CNR-IEIIT, Italy), Alessandro Nordio (CNR-IEIIT, Italy) and Carla Fabiana Chiasserini (Politecnico di Torino, Italy)

Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communications security and privacy. In this work, we focus on this aspect and design a solution to counteract the presence of passive eavesdroppers overhearing transmissions from a base station towards legitimate users. Unlike most of the existing works addressing passive eavesdroppring, the proposed solution has low complexity, is suitable for scenarios where nodes are equipped with a limited number of antennas, and effectively trades off the legitimate users' data rate for secrecy rate.

Age of Information in Ultra-Dense Computation-Intensive Internet of Things (IoT) Systems

Bo Zhou and Walid Saad (Virginia Tech, USA)

In this paper, a dense Internet of Things (IoT) monitoring system for computational intensive applications is studied in which a large number of devices with computing capability preprocess the collected raw status information into update packets and contend for transmitting them to the corresponding receivers, using a carrier sense multiple access (CSMA) scheme. Depending on whether the preprocessing operation completes when each device senses a channel, two policies are considered: Preprocess-then-Sense policy (PtS) and Preprocessing-while-Sensing policy (PwS). Particularly, under policy PtS, each device must complete the preprocessing operation before sensing a channel; while under policy PwS, it performs the preprocessing operation and senses a channel concurrently. Here, for policy PwS, if the preprocessing operation is incomplete while a sensed channel is available to be used, then each device will still occupy the channel by sending dummy bits. For both policies, the closed-form expressions of the average age of information (AoI) are characterized. Then, a mean-field approximation framework with guaranteed accuracy is developed to study the asymptotic performance for the considered system in the large population regime. Simulation results validate the analytical results and show that the proposed mean-field approximation under policy PtS is accurate even for a small number of devices. It is also observed that policy PtS achieves a smaller average AoI than policy PwS, revealing that it is unnecessary for each device to occupy the channel before the preprocessing operation completes.

Transmission Delay Minimization via Joint Power Control and Caching in Wireless HetNets

Derya Malak (Rensselaer Polytechnic Institute, USA), Faruk V. Mutlu, Jinkun Zhang and Edmund M. Yeh (Northeastern University, USA)

A fundamental challenge in wireless heterogeneous networks (HetNets) is to effectively use the limited transmission and storage resources in the presence of increasing deployment density and backhaul capacity constraints. To alleviate bottlenecks and reduce resource consumption, we design optimal caching and power control algorithms for multi-hop wireless HetNets. We devise a joint optimization framework to minimize the average transmission delay as a function of the caching variables and the signal-to-interference-plus-noise ratios (SINR) as determined by the transmission powers, while explicitly accounting for backhaul connection costs and the power constraints. Using convex relaxation and rounding, we obtain a reduced-complexity formulation (RCF) of the joint optimization problem, which can provide a constant factor approximation to the globally optimal solution. We characterize the necessary (KKT) conditions for an optimal solution to RCF, and use strict quasi-convexity to show that the KKT points are Pareto optimal for RCF. We then devise a subgradient projection algorithm to jointly update the caching and power variables, and show that under appropriate conditions, the algorithm converges at a linear rate to the local minima of RCF, under general SINR. We support our analytical findings with results from numerical experiments.

The Case for Small-Scale, Mobile-Enhanced COVID-19 Epidemiology

Fan Yi, Yaxiong Xie and Kyle Jamieson (Princeton University, USA)

Our understanding of COVID-19 pandemic epidemiology has many gaps, with many challenges arising on a global scale. This paper looks at the problem at a smaller geographical scale, the extent of the campus of a large organization. Equipped with an asymptomatic testing program and rough location data from the campus wireless network, we make the case that epidemiological models may be informed from this new source of data, which offers fidelity at the temporal resolution of seconds and spatial resolution of a Wi-Fi cell size, in particular for the tasks of pinpointing clusters of cases and contexts of infection transmission. We sketch the design of a system that fuses the two foregoing information streams and explain how the result can be incorporated into standard epidemiological models of communicable disease, both for better parameter estimation in elementary models, as well as for providing spatial inputs into more sophisticated models. We conclude with logistical and privacy considerations we have encountered in an associated ongoing study, to inform similar efforts at other organizations.

Session Chair

Guoliang Xue (Arizona State University, USA)

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