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A Distributed Privacy-Preserving Learning Dynamics in General Social Networks [article]

Youming Tao, Shuzhen Chen, Feng Li, Dongxiao Yu, Jiguo Yu, Hao Sheng
2020 arXiv   pre-print
In this paper, we study a distributed privacy-preserving learning problem in general social networks. Specifically, we consider a very general problem setting where the agents in a given multi-hop social network are required to make sequential decisions to choose among a set of options featured by unknown stochastic quality signals. Each agent is allowed to interact with its peers through multi-hop communications but with its privacy preserved. To serve the above goals, we propose a four-staged
more » ... distributed social learning algorithm. In a nutshell, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to its peers' perturbed latest adoptions, and iv) decides whether or not to adopt the selected option according to its latest quality signal. By our solid theoretical analysis, we provide answers to two fundamental algorithmic questions about the performance of our four-staged algorithm: on one hand, we illustrate the convergence of our algorithm when there are a sufficient number of agents in the social network, each of which are with incomplete and perturbed knowledge as input; on the other hand, we reveal the quantitative trade-off between the privacy loss and the communication overhead towards the convergence. We also perform extensive simulations to validate our theoretical analysis and to verify the efficacy of our algorithm.
arXiv:2011.09845v1 fatcat:k2wmjqht4jay5j5ogorzkfgeau

Bounded information dissemination in multi-channel wireless networks

Yu Yan, Dongxiao Yu, Yuexuan Wang, Jiguo Yu, Francis C. M. Lau
2014 Journal of combinatorial optimization  
., Chlebus et al. (2006) , Clementi et al. (2001 ), Fernandez-Anta et al. (2013 , Goldberg et al. (2004) , Kowalski (2005) , Martel (1994) , and Yu et al. (2012) .  ... 
doi:10.1007/s10878-014-9804-3 fatcat:o7h3gxxzyzb3nfkgss7hbu5elq

GCN for HIN via Implicit Utilization of Attention and Meta-paths [article]

Di Jin, Zhizhi Yu, Dongxiao He, Carl Yang, Philip S. Yu, Jiawei Han
2020 arXiv   pre-print
Yu is with the Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60661 USA. E-mail: • J.  ...  Yu and D. He are with the College of Intelligence and Computing, Tianjin University, Tianjin 300350, China. E-mail: {jindi, yuzhizhi, hedongxiao} • C.  ... 
arXiv:2007.02643v1 fatcat:k3jmh4z7lnd3fdshuvx6g4bude

Data Dissemination in Unified Dynamic Wireless Networks [article]

Magnus M. Halldorsson and Tigran Tonoyan and Yuexuan Wang and Dongxiao Yu
2016 arXiv   pre-print
Yu, Y. Wang, Y. Yan, J. Yu, and F. Lau. Speedup of information exchange using multiple channels in wireless ad hoc networks. In INFOCOM'15, 2015. SINR Model. Consider a network in a metric space.  ...  Yu, Y. Wang, Q.-S. Hua, and F. C. M. Lau. Distributed local broadcasting algorithms in the physical interference model.  ... 
arXiv:1605.02474v1 fatcat:mre5xqabgrcivla6dlgrkpwebm

A uniqueness theorem for 3D semilinear wave equations satisfying the null condition [article]

Dongxiao Yu
2021 arXiv   pre-print
Yu showed a converse theorem of the classical Huygens principle for free wave equations.  ... 
arXiv:2109.15041v1 fatcat:5irzoax7ybf5xnsnywgf7p5kce

Nontrivial global solutions to some quasilinear wave equations in three space dimensions [article]

Dongxiao Yu
2022 arXiv   pre-print
Yu [40] for a shock formation result in three space dimensions.  ... 
arXiv:2204.12870v1 fatcat:vsjxbwqc7nby5jspoqme3vf3ai

Leveraging multiple channels in ad hoc networks

Magnús M. Halldórsson, Yuexuan Wang, Dongxiao Yu
2018 Distributed computing  
Also, the interference I Yu (v) received by v is then at most T s ≤ 2 α −1 2 α N . Hence, I Yu (w) = y∈Yu P d(y, w) α ≤ 2 α I Yu (v) ≤ 2 α T s ≤ (2 α − 1)N ≤ 1 β P/r α − N , as desired.  ...  Yu et al. [36] gave a randomized algorithm with running time O(∆ log n + log 2 n) that achieves a ∆ + 1-coloring in the SINR model.  ... 
doi:10.1007/s00446-018-0329-3 fatcat:olj2sbtvwfg4rp7qawpyocvuxq

Parallel Algorithms for Core Maintenance in Dynamic Graphs [article]

Na Wang, Dongxiao Yu, Hai Jin, Chen Qian, Xia Xie, Qiang-Sheng Hua
2016 arXiv   pre-print
This paper initiates the studies of parallel algorithms for core maintenance in dynamic graphs. The core number is a fundamental index reflecting the cohesiveness of a graph, which are widely used in large-scale graph analytics. The core maintenance problem requires to update the core numbers of vertices after a set of edges and vertices are inserted into or deleted from the graph. We investigate the parallelism in the core update process when multiple edges and vertices are inserted or
more » ... Specifically, we discover a structure called superior edge set, the insertion or deletion of edges in which can be processed in parallel. Based on the structure of superior edge set, efficient parallel algorithms are then devised for incremental and decremental core maintenance respectively. To the best of our knowledge, the proposed algorithms are the first parallel ones for the fundamental core maintenance problem. The algorithms show a significant speedup in the processing time compared with previous results that sequentially handle edge and vertex insertions/deletions. Finally, extensive experiments are conducted on different types of real-world and synthetic datasets, and the results illustrate the efficiency, stability and scalability of the proposed algorithms.
arXiv:1612.09368v1 fatcat:5oadmysvjvdzdbqdcwhkpkwoa4

Distributed wireless link scheduling in the SINR model

Dongxiao Yu, Yuexuan Wang, Qiangsheng Hua, Jiguo Yu, Francis C. M. Lau
2015 Journal of combinatorial optimization  
We present an approximation algorithm for wireless link scheduling under the physical SINR interference model. In the link scheduling problem, it is given a set of n links in a metric space, each of which is a sender-receiver pair, and the objective is to schedule the links using the minimum amount of time. We focus on a variant of this fundamental problem where the power is fixed, i.e., the power assignment of links is given as part of the input. Specifically, we consider an important category
more » ... of power assignments called length-monotone sublinear power assignment, which includes the widely studied uniform, mean and linear power assignments. We present a distributed
doi:10.1007/s10878-015-9876-8 fatcat:htu35o7tcrgxdlqqqilhnccj3a

Decentralized Wireless Federated Learning with Differential Privacy [article]

Shuzhen Chen, Dongxiao Yu, Yifei Zou, Jiguo Yu, Xiuzhen Cheng
2022 arXiv   pre-print
This paper studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a Decentralized-Wireless-Federated-Learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers
more » ... change their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies (ϵ,δ)-differential privacy and the privacy budget per worker scales as 𝒪(1/√(N)), in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of 𝒪(√(1/TN)) as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.
arXiv:2109.09142v3 fatcat:2yuxqusdizgbrcgkpiod5vne74

Information exchange with collision detection on multiple channels

Yuepeng Wang, Yuexuan Wang, Dongxiao Yu, Jiguo Yu, Francis C. M. Lau
2014 Journal of combinatorial optimization  
, in a stochastic model Goldberg et al. (2004) , in adversarial queuing models Bender et al. (2005) , Chlebus et al. (2006) and Kowalski (2005) , and message arrivals determined by an adversary Yu  ... 
doi:10.1007/s10878-014-9713-5 fatcat:phaguko5g5bppbqezp3qidt3be

A Local Energy Consumption Prediction-Based Clustering Protocol for Wireless Sensor Networks

Jiguo Yu, Li Feng, Lili Jia, Xin Gu, Dongxiao Yu
2014 Sensors  
Clustering is a fundamental and effective technique for utilizing sensor nodes' energy and extending the network lifetime for wireless sensor networks. In this paper, we propose a novel clustering protocol, LECP-CP (local energy consumption prediction-based clustering protocol), the core of which includes a novel cluster head election algorithm and an inter-cluster communication routing tree construction algorithm, both based on the predicted local energy consumption ratio of nodes. We also
more » ... ide a more accurate and realistic cluster radius to minimize the energy consumption of the entire network. The global energy consumption can be optimized by the optimization of the local energy consumption, and the energy consumption among nodes can be balanced well. Simulation results validate our theoretical analysis and show that LECP-CP has high efficiency of energy utilization, good scalability and significant improvement in the network lifetime. In the choice of CHs, the criteria of existing clustering algorithms are different. We summarize the previous work about the selection methods of CHs as follows. Low energy adaptive clustering hierarchy (LEACH) [23, 24] randomly rotates the CHs to distribute the energy load among all of the sensor nodes in the network. The CHs' selection of them uses a probability scheme by which each node determines whether it is selected to be the CH based on the random number generated by itself. Although LEACH is simple and does not require a large communication overhead, it does not consider the energy and the distribution of CHs, which makes the algorithm energy-inefficient. Different from LEACH, DCHSintroduces the residual energy of nodes into the probability threshold, which improves the energy efficiency of the entire network and can extend the network lifetime effectively. Some other similar cluster head election algorithms based on the residual energy of nodes are also proposed in [25] [26] [27] [28] [29] . The algorithms proposed in [26, 30, 31] are the centralization version of LEACH. They improve LEACH by using central control, that is the BS collects information, such as node energy and location, from all sensor nodes and selects the optimal CHs. The defects of these are that the clustering process may be very complex and can generate more overhead. Thus, the centralized clustering algorithm has poor scalability and is only suitable for small or medium-sized networks. For this reason, most of the effective clustering algorithms are all distributed. Hybrid energy efficient distributed clustering (HEED) [32] is a distributed clustering algorithm, in which CHs are selected from the sensor nodes based on a certain probability related to a hybrid of energy and communication cost. Only sensor nodes with high residual energy and lower intra-communication costs can become CHs. Clusters generated by HEED are more well-distributed than LEACH. However, it cannot guarantee the optimal number of elected CHs and the network connectivity. A similar, but improved, clustering algorithm, EEDC, is proposed in [33] , which can reduce the number of iterations and prolong the network lifetime efficiently. An energy-aware data gathering protocol for wireless sensor networks ( EADEEG) [34] is a novel distributed clustering algorithm. It elects cluster heads based on the ratio between the average residual energy of neighbor nodes and the residual energy of the node itself, which can achieve a good CH distribution and prolong the network lifetime. However, in some cases, there are "isolate points" in EADEEG, which influence the monitoring performance and lifetime of networks. In addition, it chooses 2R a as the inter-cluster communication radius, where R a denotes the cluster radius, which cannot ensure the connectivity among CHs. Similar to EADEEG, but improved, cluster head election algorithms are adopted in the cluster setup phase of clustering algorithms [35] [36] [37] , which can effectively solve the "isolate points" problem existing in EADEEG, and the CHs generated by them can cover all the nodes in the network. For the disconnected problem, two energy-efficient clustering algorithms, called a distributed algorithm of clustering technology based on parameters used for electing CHs (BPEC) [38]
doi:10.3390/s141223017 pmid:25479330 pmcid:PMC4299051 fatcat:r7zn4ugocjbsdjiwomwliblfoa

Efficient Link Scheduling Solutions for the Internet of Things Under Rayleigh Fading

Kan Yu, Jiguo Yu, Xiuzhen Cheng, Dongxiao Yu, Anming Dong
2021 IEEE/ACM Transactions on Networking  
Moreover, hexagon partition based link scheduling algorithms were proposed by Yu et al. [18] .  ... 
doi:10.1109/tnet.2021.3093306 fatcat:zdmbl7phxncfzd7t67zw5d7twq

Efficient Link Scheduling in Wireless Networks under Rayleigh-fading and Multiuser Interference

Jiguo Yu, Kan Yu, Dongxiao Yu, Weifeng Lv, Xiuzhen Cheng, Honglong Chen, Wei Cheng
2020 IEEE Transactions on Wireless Communications  
In [31] , Yu et al. showed that the lower bound can be broken in some special cases.  ... 
doi:10.1109/twc.2020.2994998 fatcat:mpbwabjog5fjpgzkcao77eeyla

Efficient distributed multiple-message broadcasting in unstructured wireless networks

Dongxiao Yu, Qiang-Sheng Hua, Yuexuan Wang, Jiguo Yu, Francis C. M. Lau
2013 2013 Proceedings IEEE INFOCOM  
Multiple-message broadcast is a generalization of the traditional broadcast problem. It is to disseminate k distinct (1 ≤ k ≤ n) messages stored at k arbitrary nodes to the entire network with the fewest timeslots. In this paper, we study this basic communication primitive in unstructured wireless networks under the physical interference model (also known as the SINR model). The unstructured wireless network assumes unknown network topology, no collision detection and asynchronous
more » ... . Our proposed randomized distributed algorithm can accomplish multiple-message broadcast in O((D + k) log n + log 2 n) timeslots with high probability, where D is the network diameter and n is the number of nodes in the network. To our best knowledge, this work is the first one to consider distributively implementing multiplemessage broadcasting in unstructured wireless networks under a global interference model, which may shed some light on how to efficiently solve in general a "global" problem in a "local" fashion with "global" interference constraints in asynchronous wireless ad hoc networks. Apart from the algorithm, we also show an Ω(D+k+log n) lower bound for randomized distributed multiple message broadcast algorithms under the assumed network model.
doi:10.1109/infcom.2013.6567048 dblp:conf/infocom/YuHWYL13 fatcat:w3tqzjpnivfelijudn5ep4zjiq
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