Handbook of Trustworthy Federated Learning

Edited by My T. Thai (), Hai N. Phan () and Bhavani Thuraisingham ()

in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu

Date: 2025
ISBN: 978-3-031-58923-2
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Chapters in this book:

Trustworthiness, Privacy, and Security in Federated Learning

Sisi Zhou, Lijun Xiao, Yufeng Xiao and Meikang Qiu

Secure Federated Learning

Bo Tang and Xingyu Li

Data Poisoning and Leakage Analysis in Federated Learning

Wenqi Wei, Tiansheng Huang, Zachary Yahn, Anoop Singhal, Margaret Loper and Ling Liu

Robust Federated Learning Against Targeted Attackers Using Model Updates Correlation

Priyesh Ranjan, Ashish Gupta and Sajal K. Das

Unfair Trojan: Targeted Backdoor Attacks Against Model Fairness

Nicholas Furth, Abdallah Khreishah, Guanxiong Liu, NhatHai Phan and Yasser Jararweh

Federated Bilevel Optimization

Hongchang Gao

A Two-Stage Stochastic Programming Approach for the Key Management q-Composite Scheme

Maciej Rysz, Guanglin Xu and Alexander Semenov

Recent Advances in Federated Graph Learning

Tre’ R. Jeter and My T. Thai

Privacy in Federated Learning Natural Language Models

Phung Lai and C. Ariel Pinto

Federated Learning of Models Pretrained on Different Features with Consensus Graphs

Tengfei Ma, Jie Chen and Trong Nghia Hoang

Robust Federated Learning for Edge Intelligence

Dongxiao Yu, Xiao Zhang, Hanshu He, Shuzhen Chen, Jing Qiao, Yangyang Wang and Xiuzhen Cheng

ZoneFL: Zone-Based Federated Learning at the Edge

Xiaopeng Jiang, Hessamaldin Mohammadi, Cristian Borcea and NhatHai Phan

Synthetic Data for Privacy Preservation in Distributed Data Analysis Systems

Anantaa Kotal, Sai Sree Laya Chukkapalli and Anupam Joshi

Toward Green Federated Learning

Minsu Kim and Walid Saad

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DOI: 10.1007/978-3-031-58923-2

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