Personalized federated learning with dynamic clustering and model distillation

Personalized federated learning with dynamic clustering and model distillation

Federated learning is a distributed machine learning technique that allows various data sources to work together to train models while keeping their raw data private. However, federated learning faces many challenges when dealing with non-independent and identically distributed (Non-IID) data, espec...

Saved in:

書目詳細資料
主要作者:	Bao, Junyan
其他作者:	Tay Wee Peng
格式:	Thesis-Master by Coursework
語言:	English
出版:	Nanyang Technological University 2025
主題:	Computer and Information Science Federated learning Hierarchical clustering Knowledge distillation
在線閱讀:	https://hdl.handle.net/10356/181935
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

相似書籍

An empirical study of the inherent resistance of knowledge distillation based federated learning to targeted poisoning attacks
由: He, Weiyang, et al.
出版: (2024)

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

FedART: A neural model integrating federated learning and adaptive resonance theory
由: PATERIA, Shubham, et al.
出版: (2025)

Effective intrusion detection in heterogeneous Internet-of-Things networks via ensemble knowledge distillation-based federated learning
由: Shen, Jiyuan, et al.
出版: (2024)

Edge-computing-based knowledge distillation and multitask learning for partial discharge recognition
由: Ji, Jinsheng, et al.
出版: (2024)

FedTKD: a trustworthy heterogeneous federated learning based on adaptive knowledge distillation
由: Chen, Leiming, et al.
出版: (2024)

Dynamic spectrum access for Internet-of-Things with hierarchical federated deep reinforcement learning
由: Zhang, Songbo, et al.
出版: (2023)

Federated-learning-based wireless traffic prediction
由: Li, Feng, et al.
出版: (2025)

FedCIO: efficient exact federated unlearning with clustering, isolation, and one-shot aggregation
由: Qiu, Hongyu, et al.
出版: (2024)

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

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

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

BADFL: Backdoor attack defense in federated learning from local model perspective
由: ZHANG, Haiyan, 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)

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

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

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

On-the-fly knowledge distillation model for sentence embedding
由: Zhu, Xuchun
出版: (2024)

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

BadSFL: backdoor attack in scaffold federated learning
由: Zhang, Xuanye
出版: (2024)

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

A simulation platform for auction-based federated learning
由: Tang, Daryl Jun Da
出版: (2024)

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

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

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

Lightweight privacy-preserving cross-cluster federated learning with heterogeneous data
由: CHEN, Zekai, et al.
出版: (2024)

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

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)

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

Discriminator-enhanced knowledge-distillation networks
由: Li, Zhenping, et al.
出版: (2023)

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

Hercules: Boosting the performance of privacy-preserving federated learning
由: XU, Guowen, et al.
出版: (2023)

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

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

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)

Enhancing dynamic security assessment in smart grids through quantum federated learning
由: Ren, Chao, et al.
出版: (2025)

Towards fairness-aware federated learning
由: Shi, Yuxin, et al.
出版: (2024)

MarS-FL: enabling competitors to collaborate in federated learning
由: Wu, Xiaohu, et al.
出版: (2023)