MarS-FL: enabling competitors to collaborate in federated learning

MarS-FL: enabling competitors to collaborate in federated learning

Federated learning (FL) is rapidly gaining popularity and enables multiple data owners (a.k.a. FL participants) to collaboratively train machine learning models in a privacy-preserving way. A key unaddressed scenario is that these FL participants are in a competitive market, where market shares repr...

Saved in:

書目詳細資料
Main Authors:	Wu, Xiaohu, Yu, Han
其他作者:	School of Computer Science and Engineering
格式:	Article
語言:	English
出版:	2023
主題:	Engineering::Computer science and engineering Federated learning Competitive Market
在線閱讀:	https://hdl.handle.net/10356/164431
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

相似書籍

CrowdFL: a marketplace for crowdsourced federated learning
由: Feng, Daifei, et al.
出版: (2022)

EasyFL: a low-code federated learning platform for dummies
由: Zhuang, Weiming, et al.
出版: (2023)

Blockchain-enabled federated learning with mechanism design
由: Toyoda, Kentaroh, et al.
出版: (2021)

Privacy and robustness in federated learning: attacks and defenses
由: Lyu, Lingjuan, et al.
出版: (2023)

Towards federated learning in UAV-enabled internet of vehicles : a multi-dimensional contract-matching approach
由: Lim, Bryan Wei Yang, et al.
出版: (2021)

Content popularity prediction based on quantized federated Bayesian learning in fog radio access networks
由: Tao, Yunwei, et al.
出版: (2023)

Peer-to-peer federated learning
由: Sim, Nicholas Yong Yue
出版: (2024)

Federated learning for green shipping optimization and management
由: Wang, Haoqing, et al.
出版: (2023)

Auditable and verifiable federated learning based on blockchain-enabled decentralization
由: Kalapaaking, Aditya Pribadi, et al.
出版: (2024)

Semi-supervised federated heterogeneous transfer learning
由: Feng, Siwei, et al.
出版: (2022)

CaBaFL: Asynchronous federated learning via hierarchical cache and feature balance
由: XIA, Zeke, et al.
出版: (2024)

Robust semi-supervised federated learning for images automatic recognition in internet of drones
由: Zhang, Zhe, et al.
出版: (2023)

Practical attribute reconstruction attack against federated learning
由: Chen, Chen, et al.
出版: (2024)

Efficient asynchronous multi-participant vertical federated learning
由: Shi, Haoran, et al.
出版: (2024)

Covert federated learning via intelligent reflecting surfaces
由: Zheng, Jie, et al.
出版: (2023)

GTG-shapley: efficient and accurate participant contribution evaluation in federated learning
由: Liu, Zelei, et al.
出版: (2024)

Securing federated learning: a covert communication-based approach
由: Xie, Yuan-Ai, et al.
出版: (2024)

Federated learning-powered visual object detection for safety monitoring
由: Liu, Yang, et al.
出版: (2024)

Aggregating intrinsic information to enhance BCI performance through federated learning
由: Liu, Rui, et al.
出版: (2024)

Fairness-aware reverse auction-based federated learning
由: Tang, Xiaoli, et al.
出版: (2025)

Efficient and privacy-preserving feature importance-based vertical federated learning
由: Li, Anran, et al.
出版: (2024)

BlockFL: blockchain-enabled decentralized federated learning and model trading
由: Pham, Tan Anh Khoa
出版: (2022)

FlexFL: Heterogeneous federated learning via APoZ-guided flexible pruning in uncertain scenarios
由: CHEN, Zekai, et al.
出版: (2024)

A novel joint dataset and incentive management mechanism for federated learning over MEC
由: Lee, Joohyung, et al.
出版: (2023)

Federated learning in mobile edge networks : a comprehensive survey
由: Lim, Bryan Wei Yang, et al.
出版: (2020)

Dynamic edge association and resource allocation in self-organizing hierarchical federated learning networks
由: Lim, Bryan Wei Yang, et al.
出版: (2022)

Decentralized edge intelligence : a dynamic resource allocation framework for hierarchical federated learning
由: Lim, Bryan Wei Yang, et al.
出版: (2022)

Deep reinforcement learning based scheduling strategy for federated learning in sensor-cloud systems
由: Zhang, Tinghao, et al.
出版: (2023)

Membership inference vulnerabilities in peer-to-peer federated learning
由: Luqman, Alka, et al.
出版: (2024)

A blockchain based federated learning for message dissemination in vehicular networks
由: Ayaz, Ferheen, et al.
出版: (2022)

Towards efficient and certified recovery from poisoning attacks in federated learning
由: Jiang, Yu, et al.
出版: (2025)

Reimagining Federalism in India: Exploring the Frontiers of Collaborative Federal Architecture
由: Sharma, Chanchal Kumar
出版: (2015)

FlGan: GAN-based unbiased federated learning under non-IID settings
由: MA, Zhuoran, et al.
出版: (2024)

FlGan: GAN-based unbiased federated learning under non-IID settings
由: MA, Zhuoran, et al.
出版: (2024)

Multi-agent deep reinforcement learning based incentive mechanism for multi-task federated edge learning
由: Zhao, Nan, et al.
出版: (2023)

Joint client-and-sample selection for federated learning via bi-level optimization
由: Li, Anran, et al.
出版: (2024)

Distribution-balanced federated learning for fault identification of power lines
由: Wang, Tianjing, et al.
出版: (2023)

When information freshness meets service latency in federated learning : a task-aware incentive scheme for smart industries
由: Lim, Bryan Wei Yang, et al.
出版: (2021)

A cost-aware utility-maximizing bidding strategy for auction-based federated learning
由: Tang, Xiaoli, et al.
出版: (2025)

Federated graph neural networks: overview, techniques, and challenges
由: Liu, Rui, et al.
出版: (2024)