research-article

Two-Stage Competitive Particle Swarm Optimization Based Timing-Driven X-Routing for IC Design Under Smart Manufacturing

Authors:

Yeh-Cheng Chen,

Guolong ChenAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 13, Issue 4

Article No.: 41, Pages 1 - 26

https://doi.org/10.1145/3531328

Published: 10 August 2022 Publication History

Abstract

As timing delay becomes a critical issue in chip performance, there is a burning desire for IC design under smart manufacturing to optimize the delay. As the best connection model for multi-terminal nets, the wirelength and the maximum source-to-sink pathlength of the Steiner minimum tree are the decisive factors of timing delay for routing. In addition, considering that X-routing can get the utmost out of routing resources, this article proposes a Timing-Driven X-routing Steiner Minimum Tree (TD-XSMT) algorithm based on two-stage competitive particle swarm optimization. This work utilizes the multi-objective particle swarm optimization algorithm and redesigns its framework, thus improving its performance. First, a two-stage learning strategy is presented, which balances the exploration and exploitation capabilities of the particle by learning edge structures and pseudo-Steiner point choices. Especially in the second stage, a hybrid crossover strategy is designed to guarantee convergence quality. Second, the competition mechanism is adopted to select particle learning objects and enhance diversity. Finally, according to the characteristics of the discrete TD-XSMT problem, the mutation and crossover operators of the genetic algorithm are used to effectively discretize the proposed algorithm. Experimental results reveal that TSCPSO-TD-XSMT can obtain a smooth trade-off between wirelength and maximum source-to-sink pathlength, and achieve distinguished timing delay optimization.

References

[1]

Charles J. Alpert, Wing-Kai Chow, Kwangsoo Han, Andrew B. Kahng, Zhuo Li, Derong Liu, and Sriram Venkatesh. 2018. Prim-Dijkstra revisited: Achieving superior timing-driven routing trees. In Proceedings of the 2018 International Symposium on Physical Design. ACM, New York, NY, 10–17.

Digital Library

[2]

Zhou Caihong, Wu Zengyuan, and Liu Chang. 2019. A study on quality prediction for smart manufacturing based on the optimized BP-AdaBoost model. In Proceedings of the 2019 IEEE International Conference on Smart Manufacturing, Industrial, and Logistics Engineering. IEEE, Los Alamitos, CA, 1–3.

[3]

Saurav Chakraborty, Agnieszka Onuchowska, Sagar Samtani, Wolfgang Jank, and Brandon Wolfram. 2021. Machine learning for automated industrial IoT attack detection: An efficiency-complexity trade-off. ACM Transactions on Management Information System 12, 4 (2021), 1–28.

Digital Library

[4]

Baotong Chen, Jiafu Wan, Antonio Celesti, Di Li, Haider Abbas, and Qin Zhang. 2018. Edge computing in IoT-based manufacturing. IEEE Communications Magazine 56, 9 (2018), 103–109.

[5]

Gengjie Chen, Peishan Tu, and Evangeline F. Y. Young. 2017. SALT: Provably good routing topology by a novel Steiner shallow-light tree algorithm. In Proceedings of the 2017 IEEE/ACM International Conference on Computer-Aided Design. 569–576.

Digital Library

[6]

Gengjie Chen and Evangeline F. Y. Young. 2020. SALT: Provably good routing topology by a novel Steiner shallow-light tree algorithm. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, 6 (2020), 1217–1230.

[7]

Xiaohua Chen, Ruping Zhou, Genggeng Liu, Zhen Chen, and Wenzhong Guo. 2021. Timing-driven X-architecture Steiner minimum tree construction based on social learning multi-objective particle swarm optimization. In Companion Proceedings of the Web Conference 2021 (WWW’21). ACM, New York, NY, 77–84.

Digital Library

[8]

C. A. Coello Coello and M. S. Lechuga. 2002. MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation, Vol. 2. 1051–1056.

[9]

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182–197.

Digital Library

[10]

Roman Denysiuk, Lino Costa, and Isabel Espirito Santo. 2014. Clustering-based selection for evolutionary many-objective optimization. In Proceedings of the International Conference on Parallel Problem Solving from Nature, Vol. 8672. 538–547.

[11]

Pelageya I. Frolova, Rustam Zh. Chochaev, Galina A. Ivanova, and Sergey V. Gavrilov. 2020. Delay matrix based timing-driven placement for reconfigurable systems-on-chip. In Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. IEEE, Los Alamitos, CA, 1799–1803.

[12]

Wenzhong Guo and Xing Huang. 2020. PORA: A Physarum-inspired obstacle-avoiding routing algorithm for integrated circuit design. Applied Mathematical Modelling 78 (2020), 268–286.

[13]

Yicun Hua, Yaochu Jin, and Kuangrong Hao. 2019. A clustering-based adaptive evolutionary algorithm for multiobjective optimization with irregular Pareto fronts. IEEE Transactions on Cybernetics 49, 7 (2019), 2758–2770.

[14]

Tao Huang and Evangeline F. Y. Young. 2012. Construction of rectilinear Steiner minimum trees with slew constraints over obstacles. In Proceedings of the International Conference on Computer-Aided Design. ACM, New York, NY, 144–151.

Digital Library

[15]

Xing Huang, Wenzhong Guo, and Guolong Chen. 2015. Fast obstacle-avoiding octilinear Steiner minimal tree construction algorithm for VLSI design. In Proceedings of the 16th International Symposium on Quality Electronic Design. IEEE, Los Alamitos, CA, 46–50.

[16]

Xing Huang, Tsung-Yi Ho, Zepeng Li, Genggeng Liu, Lu Wang, Qingshan Li, Wenzhong Guo, Bing Li, and Ulf Schlichtmann. 2022. MiniControl 2.0: Co-synthesis of flow and control layers for microfluidic biochips with strictly constrained control ports. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. Early access, March 8, 2022.

[17]

Xing Huang, Genggeng Liu, Wenzhong Guo, and Guolong Chen. 2013. Obstacle-avoiding octagonal Steiner tree construction based on particle swarm optimization. In Proceedings of the 2013 9th International Conference on Natural Computation. IEEE, Los Alamitos, CA, 539–543.

[18]

Xing Huang, Genggeng Liu, Wenzhong Guo, Yuzhen Niu, and Guolong Chen. 2015. Obstacle-avoiding algorithm in X-architecture based on discrete particle swarm optimization for VLSI design. ACM Transactions on Design Automation of Electronic Systems 20, 2 (2015), 1–28.

Digital Library

[19]

Arif Iqbal and Girish Kumar Singh. 2021. PSO based controlled six-phase grid connected induction generator for wind energy generation. CES Transactions on Electrical Machines and Systems 5, 1 (2021), 41–49.

[20]

Y. I. Ismail, E. G. Friedman, and J. L. Neves. 2000. Equivalent Elmore delay for RLC trees. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 19, 1 (2000), 83–97.

Digital Library

[21]

Shubhi Katiyar and Ashish Dutta. 2021. PSO based path planning and dynamic obstacle avoidance in CG space of a 10 DOF Rover. In Advances in Robotics—5th International Conference of the Robotics Society (AIR’21). Article 5, 6 pages ACM, New York, NY, 1–6.

Digital Library

[22]

J. Kennedy and R. Eberhart. 1995. Particle swarm optimization. In Proceedings of the International Conference on Neural Networks (ICNN’95), Vol. 4. IEEE, Los Alamitos, CA, 1942–1948.

[23]

Won-Jin Kim, Byung-Gyu Ahn, Ki-Seok Chung, Jong-Wha Chung, and Sung-Hwan Oh. 2008. Timing driven force-directed floorplanning with incremental static timing analyzer. In Proceedings of the 2008 IEEE Asia Pacific Conference on Circuits and Systems. IEEE, Los Alamitos, CA, 1000–1003.

[24]

Wang Lei, Wang Yong, Yang Haigen, Yu Hongyan, Xu Wenting, Han Longbao, and Jia Kejia. 2018. Research on application of virtual-real fusion technology in smart manufacturing. In Proceedings of the 2018 IEEE 9th International Conference on Software Engineering and Service Science. IEEE, Los Alamitos, CA, 1066–1069.

[25]

Jiewu Leng, Shide Ye, Man Zhou, J. Leon Zhao, Qiang Liu, Wei Guo, Wei Cao, and Leijie Fu. 2021. Blockchain-secured smart manufacturing in industry 4.0: A survey. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, 1 (2021), 237–252.

[26]

Ke Li, Renzhi Chen, Guangtao Fu, and Xin Yao. 2019. Two-archive evolutionary algorithm for constrained multiobjective optimization. IEEE Transactions on Evolutionary Computation 23, 2 (2019), 303–315.

[27]

Liangzhi Li, Kaoru Ota, and Mianxiong Dong. 2018. Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics 14, 10 (2018), 4665–4673.

[28]

Genggeng Liu, Xiaohua Chen, Ruping Zhou, Saijuan Xu, Yeh-Cheng Chen, and Guolong Chen. 2021. Social learning discrete particle swarm optimization based two-stage X-routing for IC design under intelligent edge computing architecture. Applied Soft Computing 104 (2021), 107215.

Digital Library

[29]

Genggeng Liu, Zhisheng Chen, Zhen Zhuang, Wenzhong Guo, and Guolong Chen. 2020. A unified algorithm based on HTS and self-adapting PSO for the construction of octagonal and rectilinear SMT. Soft Computing 24, 6 (2020), 3943–3961.

Digital Library

[30]

Genggeng Liu, Wenzhong Guo, Yuzhen Niu, Guolong Chen, and Xing Huang. 2015. A PSO-based timing-driven octilinear Steiner tree algorithm for VLSI routing considering bend reduction. Soft Computing 19, 5 (2015), 1153–1169.

Digital Library

[31]

Genggeng Liu, Xing Huang, Wenzhong Guo, Yuzhen Niu, and Guolong Chen. 2015. Multilayer obstacle-avoiding X-architecture Steiner minimal tree construction based on particle swarm optimization. IEEE Transactions on Cybernetics 45, 5 (2015), 1003–1016.

[32]

Genggeng Liu, Liliang Yang, Saijuan Xu, Zuoyong Li, Yeh-Cheng Chen, and Chi-Hua Chen. 2021. X-architecture steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution. PeerJ Computer Science 7 (2021), e473.

[33]

Genggeng Liu, Xinghai Zhang, Wenzhong Guo, Xing Huang, Wen-Hao Liu, Kai-Yuan Chao, and Ting-Chi Wang. 2021. Timing-aware layer assignment for advanced process technologies considering via pillars. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. Early access, July 26, 2021.

[34]

Genggeng Liu, Weida Zhu, Saijuan Xu, Zhen Zhuang, Yeh-Cheng Chen, and Guolong Chen. 2020. Efficient VLSI routing algorithm employing novel discrete PSO and multi-stage transformation. Journal of Ambient Intelligence and Humanized Computing. Published November 13, 2020.

[35]

Genggeng Liu, Yuhan Zhu, Saijuan Xu, Yeh-Cheng Chen, and Hao Tang. 2022. PSO-based power-driven X-routing algorithm in semiconductor design for predictive intelligence of IoT applications. Applied Soft Computing 114 (2022), 108114.

Digital Library

[36]

J. C. Liu, S. Mukhopadhyay, Amit Kundu, S. H. Chen, H. C. Wang, D. S. Huang, J. H. Lee, et al. 2020. A reliability enhanced 5nm CMOS technology featuring 5th generation FinFET with fully-developed EUV and high mobility channel for mobile SoC and high performance computing application. In Proceedings of the 2020 IEEE International Electron Devices Meeting. IEEE, Los Alamitos, CA, 9.2.1–9.2.4.

[37]

Zhihan Lv, Ranran Lou, Hailin Feng, Dongliang Chen, and Haibin Lv. 2021. Novel machine learning for big data analytics in intelligent support information management systems. ACM Transactions on Management Information Systems 13, 1 (2021), 1–21.

Digital Library

[38]

A. J. Nebro, J. J. Durillo, J. Garcia-Nieto, C. A. Coello Coello, F. Luna, and E. Alba. 2009. SMPSO: A new PSO-based metaheuristic for multi-objective optimization. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making. 66–73.

[39]

Sagar Samtani, Murat Kantarcioglu, and Hsinchun Chen. 2020. Trailblazing the artificial intelligence for cybersecurity discipline: A multi-disciplinary research roadmap. ACM Transactions on Management Information System 11, 4 (2020), 1–19.

Digital Library

[40]

Song Pu Shang and Tong Jing. 2007. Steiner minimal trees in rectilinear and octilinear planes. Acta Mathematica Sinica, English Series 23, 9 (2007), 1577–1586.

[41]

Jiho Song, Cheoljon Jang, Kyungin Cho, Seungryeol Go, and Jongwha Chong. 2014. Timing driven global router with a pin partition method for 3D stacked integrated circuits. In Proceedings of the 18th IEEE International Symposium on Consumer Electronics. IEEE, Los Alamitos, CA, 1–2.

[42]

Fei Tao, Qinglin Qi, Ang Liu, and Andrew Kusiak. 2018. Data-driven smart manufacturing. Journal of Manufacturing Systems 48 (2018), 157–169.

[43]

Steven L. Teig. 2002. The X-architecture: Not your father’s diagonal wiring. In Proceedings of the 2002 International Workshop on System-Level Interconnect Prediction. ACM, New York, NY, 33–37.

Digital Library

[44]

Ye Tian, Yajie Zhang, Yansen Su, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin. 2021. Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization. IEEE Transactions on Cybernetics. Early access, March 17, 2021.

[45]

Ye Tian, Xiutao Zheng, Xingyi Zhang, and Yaochu Jin. 2020. Efficient large-scale multiobjective optimization based on a competitive swarm optimizer. IEEE Transactions on Cybernetics 50, 8 (2020), 3696–3708.

[46]

Vimal L. Vachhani, Vipul K. Dabhi, and Harshadkumar B. Prajapati. 2016. Improving NSGA-II for solving multi objective function optimization problems. In Proceedings of the 2016 International Conference on Computer Communication and Informatics. IEEE, Los Alamitos, CA, 1–6.

[47]

Yujia Wang and Yupu Yang. 2009. Particle swarm optimization with preference order ranking for multi-objective optimization. Information Sciences 179, 12 (2009), 1944–1959.

Digital Library

[48]

David Warme, Pawel Winter, and Martin Zachariasen. 2003. GeoSteiner Software for Computing Steiner Trees. Retrieved April 30, 2022 from http://geosteiner.net.

[49]

Dazhong Wu, Connor Jennings, Janis Terpenny, Robert X. Gao, and Soundar Kumara. 2017. A comparative study on machine learning algorithms for smart manufacturing: Tool wear prediction using random forests. Journal of Manufacturing Science and Engineering 139, 7 (2017), Article 071018, 9 pages. 071018

[50]

Hailin Wu, Saijuan Xu, Zhen Zhuang, and Genggeng Liu. 2019. X-architecture Steiner minimal tree construction based on discrete differential evolution. In Proceedings of the International Conference on Natural Computation, Fuzzy Systems, and Knowledge Discovery. 433–442.

[51]

Hu Xiong, Yi Wang, Wenchao Li, and Chien-Ming Chen. 2019. Flexible, efficient, and secure access delegation in cloud computing. ACM Transactions on Management Information System 10, 1 (2019), 1–20.

Digital Library

[52]

Jin-Tai Yan. 2008. Timing-driven octilinear Steiner tree construction based on Steiner-point reassignment and path reconstruction. ACM Transactions on Design Automation of Electronic Systems 13, 2 (2008), 1–18.

Digital Library

[53]

Jin-Tai Yan, Zhi-Wei Chen, and Dun-Hao Hu. 2008. Timing-driven Steiner tree construction for three-dimensional ICs. In Proceedings of the 2008 Joint 6th International IEEE Northeast Workshop on Circuits and Systems and TAISA Conference. 335–338.

[54]

Jin-Tai Yan, Shi-Qin Huang, and Zhi-Wei Chen. 2007. Top-down-based timing-driven Steiner tree construction with wire sizing and buffer insertion. In Proceedings of the 2007 IEEE Region 10 Conference (TENCON’07). IEEE, Los Alamitos, CA, 1–4.

[55]

Jin-Tai Yan, Tzu-Ya Wang, and Yu-Cheng Lee. 2005. Timing-driven Steiner tree construction based on feasible assignment of hidden Steiner points. In Proceedings of the 2005 IEEE International Symposium on Circuits and Systems, Vol. 2. 1370–1373.

[56]

Xianyu Zhang and Xinguo Ming. 2021. An implementation for smart manufacturing information system (SMIS) from an industrial practice survey. Computers & Industrial Engineering 151 (2021), 106938.

[57]

Xingyi Zhang, Ye Tian, Ran Cheng, and Yaochu Jin. 2015. An efficient approach to nondominated sorting for evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation 19, 2 (2015), 201–213.

Digital Library

[58]

Xingyi Zhang, Xiutao Zheng, Ran Cheng, Jianfeng Qiu, and Yaochu Jin. 2018. A competitive mechanism based multi-objective particle swarm optimizer with fast convergence. Information Sciences 427 (2018), 63–76.

Digital Library

[59]

Ruping Zhou, Genggeng Liu, Wenzhong Guo, and Xin Wang. 2021. An X-architecture SMT algorithm based on competitive swarm optimizer. In Proceedings of the International Conference on Web Information Systems and Applications. 393–404.

Digital Library

[60]

Qingling Zhu, Qiuzhen Lin, Weineng Chen, Ka-Chun Wong, Carlos A. Coello Coello, Jianqiang Li, Jianyong Chen, and Jun Zhang. 2017. An external archive-guided multiobjective particle swarm optimization algorithm. IEEE Transactions on Cybernetics 47, 9 (2017), 2794–2808.

Cited By

Zheng KChua HHerschel MJagadish HOoi BYip JSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Exploiting negative samplesProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694606(61287-61320)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694606
Huang RShirani FLuo DWooldridge MDy JNatarajan S(2024)Factorized explainer for graph neural networksProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i11.29157(12626-12634)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i11.29157
Agrawal NSirohi AKumar SJayadeva Wooldridge MDy JNatarajan S(2024)No prejudice! fair federated graph neural networks for personalized recommendationProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i10.28950(10775-10783)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i10.28950
Show More Cited By

Index Terms

Two-Stage Competitive Particle Swarm Optimization Based Timing-Driven X-Routing for IC Design Under Smart Manufacturing
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)
      1. Wire routing
  2. Power and energy
    1. Power estimation and optimization

Recommendations

Performance-Driven X-Architecture Routing Algorithm for Artificial Intelligence Chip Design in Smart Manufacturing
The new 7-nm Artificial Intelligence (AI) chip is an important milestone recently announced by the IBM research team, with a very important optimization goal of performance. This chip technology can be extended to various business scenarios in the ...
Social learning discrete Particle Swarm Optimization based two-stage X-routing for IC design under Intelligent Edge Computing architecture
Abstract
One of the core features of Intelligent Edge Computing (IEC) is real-time decision making, therefore low delay is more important for IC design under IEC architecture. And in very large scale integration routing, wirelength is one of ...
Graphical abstract

Display Omitted
Highlights
- Better satisfying the high-performance requirement for IC design under IEC.
- ...
Timing-driven X-architecture routing tree construction among rectangular and non-rectangular obstacles

In this paper, we formulate a new X-architecture routing problem in presence of non-rectangular obstacles, and propose an X-architecture timing-driven routing algorithm to minimize the maximum source-to-sink delay and the total wirelength ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 13, Issue 4

December 2022

255 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3555789

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2022

Online AM: 22 April 2022

Accepted: 01 April 2022

Revised: 01 April 2022

Received: 01 January 2022

Published in TMIS Volume 13, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Natural Science Foundation of China
State Key Laboratory of Computer Architecture (ICT, CAS)
Fujian Natural Science Funds
Fuzhou University

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
317
Total Downloads

Downloads (Last 12 months)75
Downloads (Last 6 weeks)5

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zheng KChua HHerschel MJagadish HOoi BYip JSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Exploiting negative samplesProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694606(61287-61320)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694606
Huang RShirani FLuo DWooldridge MDy JNatarajan S(2024)Factorized explainer for graph neural networksProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i11.29157(12626-12634)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i11.29157
Agrawal NSirohi AKumar SJayadeva Wooldridge MDy JNatarajan S(2024)No prejudice! fair federated graph neural networks for personalized recommendationProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i10.28950(10775-10783)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i10.28950
Li ZDing BYao LLi YXiao XZhou J(2024)Performance-Based Pricing for Federated Learning via AuctionProceedings of the VLDB Endowment10.14778/3648160.364816917:6(1269-1282)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648169
Liang RJiang YZhu FCheng LLiu H(2024)Defending Federated Recommender Systems against Untargeted Attacks: A Contribution-Aware Robust Aggregation SchemeACM Transactions on Knowledge Discovery from Data10.1145/370611219:1(1-28)Online publication date: 28-Nov-2024
https://dl.acm.org/doi/10.1145/3706112
Sun PWu LWang ZLiu JLuo JJin W(2024)A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price CompetitionProceedings of the ACM on Management of Data10.1145/36771272:4(1-27)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677127
Sun HTu ZSui DZhang BXu X(2024)A Federated Social Recommendation Approach with Enhanced Hypergraph Neural NetworkACM Transactions on Intelligent Systems and Technology10.1145/366593116:1(1-23)Online publication date: 30-Dec-2024
https://dl.acm.org/doi/10.1145/3665931
Sharma KLee YNambi SSalian AShah SKim SKumar S(2024)A Survey of Graph Neural Networks for Social Recommender SystemsACM Computing Surveys10.1145/366182156:10(1-34)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3661821
Hao YChen XLyu XLiu JZhu YWan ZMauw SWang WLuo BLiao XXu JKirda ELie D(2024)Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated RecommendationProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3670365(2889-2903)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3670365
Jian MBai YFu XGuo JShi GWu L(2024)Counterfactual Graph Convolutional Learning for Personalized RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365563215:4(1-20)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3655632
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents