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Jianwei Huang

Publications and source records attributed to Jianwei Huang.

At least 55 records · Page 3Linked to original sources
arXiv2023

Information Elicitation from Decentralized Crowd Without Verification

Information Elicitation Without Verification (IEWV) refers to the problem of eliciting high-accuracy solutions from crowd members when the ground truth is unverifiable. A high-accuracy team solution (aggregated from members' solutions) requires members' effort exertion, which should be incentivized properly. Previous research on IEWV mainly focused on scenarios where a central entity (e.g., the crowdsourcing platform) provides incentives to motivate crowd members. Still, the proposed designs do not apply to practical situations where no central entity exists. This paper studies the overlooked decentralized IEWV scenario, where crowd members act as both incentive contributors and task solvers. We model the interactions among members with heterogeneous team solution accuracy valuations as a two-stage game, where each member decides her incentive contribution strategy in Stage 1 and her effort exertion strategy in Stage 2. We analyze members' equilibrium behaviors under three incentive allocation mechanisms: Equal Allocation (EA), Output Agreement (OA), and Shapley Value (SV). We show that at an equilibrium under any allocation mechanism, a low-valuation member exerts no more effort than a high-valuation member. Counter-intuitively, a low-valuation member provides incentives to the collaboration while a high-valuation member does not at an equilibrium under SV. This is because a high-valuation member who values the aggregated team solution more needs fewer incentives to exert effort. In addition, when members' valuations are sufficiently heterogeneous, SV leads to team solution accuracy and social welfare no smaller than EA and OA.

cs.GT↗
arXiv2023

Incentive Mechanism Design for Distributed Ensemble Learning

Distributed ensemble learning (DEL) involves training multiple models at distributed learners, and then combining their predictions to improve performance. Existing related studies focus on DEL algorithm design and optimization but ignore the important issue of incentives, without which self-interested learners may be unwilling to participate in DEL. We aim to fill this gap by presenting a first study on the incentive mechanism design for DEL. Our proposed mechanism specifies both the amount of training data and reward for learners with heterogeneous computation and communication costs. One design challenge is to have an accurate understanding regarding how learners' diversity (in terms of training data) affects the ensemble accuracy. To this end, we decompose the ensemble accuracy into a diversity-precision tradeoff to guide the mechanism design. Another challenge is that the mechanism design involves solving a mixed-integer program with a large search space. To this end, we propose an alternating algorithm that iteratively updates each learner's training data size and reward. We prove that under mild conditions, the algorithm converges. Numerical results using MNIST dataset show an interesting result: our proposed mechanism may prefer a lower level of learner diversity to achieve a higher ensemble accuracy.

cs.GT↗
arXiv2023

Federated Learning in Competitive EV Charging Market

Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on developing FL algorithms, the effect of FL on the charging stations' operation in terms of price competition has yet to be fully understood. This paper aims to fill this gap by modeling the strategic interactions between two charging stations and EV owners as a multi-stage game. Each station first decides its FL participation strategy and charging price, and then individual EV owners decide their charging strategies. The game analysis involves solving a non-concave problem and by decomposing it into a piece-wise concave program we manage to fully characterize the equilibrium. Based on real-world datasets, our numerical results reveal an interesting insight: even if FL improves QoS, it can lead to smaller profits for both stations. The key reason is that FL intensifies the price competition between charging stations by improving stations' QoS to a similar level. We further show that the stations will participate in FL when their data distributions are mildly dissimilar.

cs.GT↗
arXiv2023

Information Disclosure under Competition in Sharing Systems

Sharing systems have facilitated the redistribution of underused resources by providing convenient online marketplaces for individual sellers and buyers. However, sellers in these systems may not fully disclose the information of their shared commodities, due to strategic behaviors or privacy concerns. Sellers' strategic information disclosure significantly affects buyers' user experiences and systems' reputation. This paper presents the first analytical study on information disclosure and pricing of competing sellers in sharing systems. In particular, we propose a two-stage game framework to capture sellers' strategic behaviors and buyers' decisions. Although the optimization problem is challenging due to sellers' non-convex and non-monotonic objectives, we completely characterize the complex market equilibria by decomposing it into several tractable subproblems. We demonstrate that full disclosure by all sellers or non-disclosure by all sellers will both lead to intense price competition. The former all-disclosure case is never an equilibrium even when all sellers have good commodity qualities and low privacy costs, while the latter non-disclosure case can be an equilibrium under which all sellers get zero profit. We also reveal several critical factors that affect sellers' information disclosure. Interestingly, sellers' sharing capacity limitation and buyers' estimation biases encourage information disclosure as they mitigate sellers' competition.

cs.GT↗
arXiv2023

Towards Carbon Transparency: A High-Resolution Carbon Emissions Database for China's Listed Companies

The dual-carbon goals of China necessitate precise accounting of company carbon emissions, vital for green development across all industries. Not only the company itself but also financial investors require accurate and comprehensive company-level emissions data for climate risk management. This paper introduces the structure and methodology of the High-resolution Database for Carbon Emissions of China-listed companies, integrating three primary data sources: self-disclosed environmental data from listed companies, long-accumulated national power emission data, and regional high-precision emission data derived from multi-source satellites. The database's innovation lies in the employment of artificial intelligence (AI) algorithms to aggregate multi-source satellite data. This approach enables the precise identification of carbon emission sources and the prediction of company-level carbon emissions. Consequently, this methodology robustly cross-validates self-reported direct emissions, enhancing the accuracy and granularity of company-level emission records. Central to the database's utility includes the provision of high-resolution company carbon emission data, which is not only highly accurate but also instrumental in carbon management and emission market transactions. By offering a more nuanced and verifiable picture of company emissions, the database supports China's broader efforts to meet its ambitious dual-carbon targets and transition towards a more sustainable and environmentally responsible economy.

physics.soc-ph↗
arXiv2023

Joint Participation Incentive and Network Pricing Design for Federated Learning

Federated learning protects users' data privacy through sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server's cost by up to 24.87% comparing with the state-of-the-art benchmarks.

cs.NI↗
arXiv2023

Reversible Non-Volatile Electronic Switching in a Near Room Temperature van der Waals Ferromagnet

The ability to reversibly toggle between two distinct states in a non-volatile method is important for information storage applications. Such devices have been realized for phase-change materials, which utilizes local heating methods to toggle between a crystalline and an amorphous state with distinct electrical properties. To expand such kind of switching between two topologically distinct phases requires non-volatile switching between two crystalline phases with distinct symmetries. Here we report the observation of reversible and non-volatile switching between two stable and closely-related crystal structures with remarkably distinct electronic structures in the near room temperature van der Waals ferromagnet Fe$_{5-δ}$GeTe$_2$. From a combination of characterization techniques we show that the switching is enabled by the ordering and disordering of an Fe site vacancy that results in distinct crystalline symmetries of the two phases that can be controlled by a thermal annealing and quenching method. Furthermore, from symmetry analysis as well as first principle calculations, we provide understanding of the key distinction in the observed electronic structures of the two phases: topological nodal lines compatible with the preserved global inversion symmetry in the site-disordered phase, and flat bands resulting from quantum destructive interference on a bipartite crystaline lattice formed by the presence of the site order as well as the lifting of the topological degeneracy due to the broken inversion symmetry in the site-ordered phase. Our work not only reveals a rich variety of quantum phases emergent in the metallic van der Waals ferromagnets due to the presence of site ordering, but also demonstrates the potential of these highly tunable two-dimensional magnets for memory and spintronics applications.

cond-mat.str-el↗
arXiv2023

Kramers nodal lines and Weyl fermions in SmAlSi

Kramers nodal lines (KNLs) have recently been proposed theoretically as a special type of Weyl line degeneracy connecting time-reversal invariant momenta. KNLs are robust to spin orbit coupling and are inherent to all non-centrosymmetric achiral crystal structures, leading to unusual spin, magneto-electric, and optical properties. However, their existence in in real quantum materials has not been experimentally established. Here we gather the experimental evidence pointing at the presence of KNLs in SmAlSi, a non-centrosymmetric metal that develops incommensurate spin density wave order at low temperature. Using angle-resolved photoemission spectroscopy, density functional theory calculations, and magneto-transport methods, we provide evidence suggesting the presence of KNLs, together with observing Weyl fermions under the broken inversion symmetry in the paramagnetic phase of SmAlSi. We discuss the nesting possibilities regarding the emergent magnetic orders in SmAlSi. Our results provide a solid basis of experimental observations for exploring correlated topology in SmAlSi.

cond-mat.mtrl-sci↗
arXiv2023

Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation

Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incentivize all clients to participate in all training rounds (known as full participation). The existing FL incentive mechanisms are typically designed by stimulating a fixed subset of clients based on their data quantity or system resources. Hence, FL is performed only using this subset of clients throughout the entire training process, leading to a biased model because of data heterogeneity. This paper proposes a game theoretic incentive mechanism for FL with randomized client participation, where the server adopts a customized pricing strategy that motivates different clients to join with different participation levels (probabilities) for obtaining an unbiased and high performance model. Each client responds to the server's monetary incentive by choosing its best participation level, to maximize its profit based on not only the incurred local cost but also its intrinsic value for the global model. To effectively evaluate clients' contribution to the model performance, we derive a new convergence bound which analytically predicts how clients' arbitrary participation levels and their heterogeneous data affect the model performance. By solving a non-convex optimization problem, our analysis reveals that the intrinsic value leads to the interesting possibility of bidirectional payment between the server and clients. Experimental results using real datasets on a hardware prototype demonstrate the superiority of our mechanism in achieving higher model performance for the server as well as higher profits for the clients.

cs.GT↗
arXiv2023

Optimization Design for Federated Learning in Heterogeneous 6G Networks

With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for innovative applications and intelligent services. However, traditional machine learning (ML) approaches require centralizing the training data in the data center or cloud, raising serious user-privacy concerns. Federated learning, as an emerging distributed AI paradigm with privacy-preserving nature, is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. However, there are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenging heterogeneity issues from three aspects: incentive mechanism design, network resource management, and personalized model optimization. We also present some open problems and promising directions for future research.

cs.LG↗
arXiv2023

Real-space and reciprocal-space topology in the Eu(Ga$_{1-x}$Al$_x$)$_4$ square net system

Magnetotransport measurements on the centrosymetric square-net Eu(Ga$_{1-x}$Al$_x$)$_4$ compounds reveal evidence for both reciprocal- and real-space topology. For compositions $0.50 \leq x \leq 0.90$, several intermediate field phases are found by magnetization measurements when $H \parallel c$, where a maximum in the topological Hall effect (THE) is observed, pointing to the existence of topological (real-space topology) or non-coplanar spin textures. For $0.25 \leq x \leq 0.39$, magnetization measurements reveal an intermediate field state, but no transition is visible in the Hall measurements. For $x = 0.15$, only one magnetic transition occurs below the Néel temperature $T_N$, and no intermediate field spin reorientations are observed. The Hall effect varies smoothly before the spin-polarized (SP) state. However, in the SP state, Hall measurements reveal a large anomalous Hall effect (AHE) for all compositions, a consequence of reciprocal-space topology. Density functional theory calculations in the paramagnetic state indeed reveal a Dirac point that lies very near the Fermi energy, which is expected to split into Weyl nodes in the SP state, thereby explaining the observed AHE. These results suggest the Eu(Ga$_{1-x}$Al$_x$)$_4$ family is a rare material platform where real- and reciprocal-space topology exist in a single material platform.

cond-mat.mtrl-sci↗
arXiv2023

Ideal Weak Topological Insulator and Protected Helical Saddle Points

The paradigm of classifying three-dimensional (3D) topological insulators into strong and weak ones (STI and WTI) opens the door for the discovery of various topological phases of matter protected by different symmetries and defined in different dimensions. However, in contrast to the vast realization of STIs, very few materials have been experimentally identified as being close to WTI. Even amongst those identified, none exists with topological surface states (TSS) exposed in a global bulk band gap that is stable at all temperatures. Here we report the design and observation of an ideal WTI in a quasi-one-dimensional (quasi-1D) bismuth halide, Bi$_{4}$I$_{1.2}$Br$_{2.8}$ (BIB). Via angle-resolved photoemission spectroscopy (ARPES), we identify that BIB hosts TSS on the (100)$\prime$ side surface in the form of two anisotropic $π$-offset Dirac cones (DCs) separated in momentum while topologically dark on the (001) top surface. The ARPES data fully determine a unique side-surface Hamiltonian and thereby identify two pairs of non-degenerate helical saddle points and a series of four Lifshitz transitions. The fact that both the surface Dirac and saddle points are in the global bulk band gap of 195 meV, combined with the small Dirac velocities, nontrivial spin texture, and the near-gap chemical potential, qualifies BIB to be not only an ideal WTI but also a fertile ground for topological many-body physics.

cond-mat.mtrl-sci↗
arXiv2022

Weyl nodal ring states and Landau quantization with very large magnetoresistance in square-net magnet EuGa$_4$

Magnetic topological semimetals (TSMs) allow for an effective control of the topological electronic states by tuning the spin configuration, and therefore are promising materials for next-generation electronic and spintronic applications. Of magnetic TSMs, Weyl nodal-line (NL) semimetals likely have the most tunability, and yet they are the least experimentally studied so far due to the scarcity of material candidates. Here, using a combination of angle-resolved photoemission spectroscopy and quantum oscillation measurements, together with density functional theory calculations, we identify the square-net compound EuGa4 as a new magnetic Weyl nodal ring (NR) semimetal, in which the line nodes form closed rings in the vicinity of the Fermi level. Remarkably, the Weyl NR states show distinct Landau quantization with clear spin splitting upon application of a magnetic field. At 2 K in a field of 14 T, the transverse magnetoresistance of EuGa4 exceeds 200,000%, which is more than two orders of magnitude larger than that of other known magnetic TSMs. High field magnetoresistance measurements indicate no saturation up to 40 T. Our theoretical model indicates that the nonsaturating MR naturally arises as a consequence of the Weyl NR state. Our work thus point to the realization of Weyl NR states in square-net magnetic materials, and opens new avenues for the design of magnetic TSMs with very large magnetoresistance.

cond-mat.mtrl-sci↗
arXiv2022

Near Real-time CO$_2$ Emissions Based on Carbon Satellite and Artificial Intelligence

To limit global warming to pre-industrial levels, global governments, industry and academia are taking aggressive efforts to reduce carbon emissions. The evaluation of anthropogenic carbon dioxide (CO$_2$) emissions, however, depends on the self-reporting information that is not always reliable. Society need to develop an objective, independent, and generalized system to meter CO$_2$ emissions. Satellite CO$_2$ observation from space that reports column-average regional CO$_2$ dry-air mole fractions has gradually indicated its potential to build such a system. Nevertheless, estimating anthropogenic CO$_2$ emissions from CO$_2$ observing satellite is bottlenecked by the influence of the highly complicated physical characteristics of atmospheric activities. Here we provide the first method that combines the advanced artificial intelligence (AI) techniques and the carbon satellite monitor to quantify anthropogenic CO$_2$ emissions. We propose an integral AI based pipeline that contains both a data retrieval algorithm and a two-step data-driven solution. First, the data retrieval algorithm can generate effective datasets from multi-modal data including carbon satellite, the information of carbon sources, and several environmental factors. Second, the two-step data-driven solution that applies the powerful representation of deep learning techniques to learn to quantify anthropogenic CO$_2$ emissions from satellite CO$_2$ observation with other factors. Our work unmasks the potential of quantifying CO$_2$ emissions based on the combination of deep learning algorithms and the carbon satellite monitor.

physics.ao-ph↗
arXiv2022

Intertwined magnetism and charge density wave order in kagome FeGe

Electron correlations often lead to emergent orders in quantum materials. Kagome lattice materials are emerging as an exciting platform for realizing quantum topology in the presence of electron correlations. This proposal stems from the key signatures of electronic structures associated with its lattice geometry: flat band induced by destructive interference of the electronic wavefunctions, topological Dirac crossing, and a pair of van Hove singularities (vHSs). A plethora of correlated electronic phases have been discovered amongst kagome lattice materials, including magnetism, charge density wave (CDW), nematicity, and superconductivity. These materials can be largely organized into two types: those that host magnetism and those that host CDW order. Recently, a CDW order has been discovered in the magnetic kagome FeGe, providing a new platform for understanding the interplay between CDW and magnetism. Here, utilizing angle-resolved photoemission spectroscopy, we observe all three types of electronic signatures of the kagome lattice: flat bands, Dirac crossings, and vHSs. From both the observation of a temperature-dependent shift of the vHSs towards the Fermi level as well as guidance via first-principle calculations, we identify the presence of the vHSs near the Fermi level (EF) to be driven by the development of underlying magnetic exchange splitting. Furthermore, we show spectral evidence for the CDW order as gaps that open on the near-EF vHS bands, as well as evidence of electron-phonon coupling from a kink on the vHS band together with phonon hardening observed by inelastic neutron scattering. Our observation points to the magnetic interaction-driven band modification resulting in the formation of the CDW order, indicating an intertwined connection between the emergent magnetism and vHS charge order in this moderately-correlated kagome metal.

cond-mat.str-el↗
arXiv2022

Insurance Contract for High Renewable Energy Integration

The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty.

eess.SY↗
arXiv2022

Age-Dependent Differential Privacy

The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of \textit{age of information}. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, which may lead to unnecessary accuracy loss when trying to achieve meaningful privacy guarantees in time-varying databases. In this work, we introduce \textit{age-dependent DP}, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we establish a connection between classical DP and age-dependent DP, based on which we characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization demonstrates that \textit{aging}, i.e., using stale data inputs and/or postponing the release of outputs, can be a new strategy to protect data privacy in addition to noise injection in the traditional DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP under any publishing and aging policies. We then characterize the optimal tradeoffs between privacy risk and utility and show how this can be achieved. Finally, case studies show that to achieve a target of an arbitrarily small privacy risk in a single-query case, combing aging and noise injection only leads to a bounded accuracy loss, whereas using noise injection only (as in the benchmark case of DP) will lead to an unbounded accuracy loss.

cs.CR↗
arXiv2022

Cross-Silo Federated Learning: Challenges and Opportunities

Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private. Based on the participating clients and the model training scale, federated learning can be classified into two types: cross-device FL where clients are typically mobile devices and the client number can reach up to a scale of millions; cross-silo FL where clients are organizations or companies and the client number is usually small (e.g., within a hundred). While existing studies mainly focus on cross-device FL, this paper aims to provide an overview of the cross-silo FL. More specifically, we first discuss applications of cross-silo FL and outline its major challenges. We then provide a systematic overview of the existing approaches to the challenges in cross-silo FL by focusing on their connections and differences to cross-device FL. Finally, we discuss future directions and open issues that merit research efforts from the community.

cs.LG↗