default search action
Federated Learning 2022
- Heiko Ludwig, Nathalie Baracaldo:
Federated Learning - A Comprehensive Overview of Methods and Applications. Springer 2022, ISBN 978-3-030-96895-3 - Heiko Ludwig, Nathalie Baracaldo:
Introduction to Federated Learning. 1-23
Part I
- Yuya Jeremy Ong, Nathalie Baracaldo, Yi Zhou:
Tree-Based Models for Federated Learning Systems. 27-52 - Shalisha Witherspoon, Dean Steuer, Nirmit Desai:
Semantic Vectorization: Text- and Graph-Based Models. 53-70 - Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun:
Personalization in Federated Learning. 71-98 - Pengqian Yu, Achintya Kundu, Laura Wynter, Shiau Hong Lim:
Personalized, Robust Federated Learning with Fed+. 99-123 - Gauri Joshi, Shiqiang Wang:
Communication-Efficient Distributed Optimization Algorithms. 125-143 - Mikhail Yurochkin, Yuekai Sun:
Communication-Efficient Model Fusion. 145-176 - Annie Abay, Yi Zhou, Nathalie Baracaldo, Heiko Ludwig:
Federated Learning and Fairness. 177-191
Part II
- Syed Zawad, Feng Yan, Ali Anwar:
Introduction to Federated Learning Systems. 195-212 - Syed Zawad, Feng Yan, Ali Anwar:
Local Training and Scalability of Federated Learning Systems. 213-233 - Syed Zawad, Feng Yan, Ali Anwar:
Straggler Management. 235-258 - Syed Zawad, Feng Yan, Ali Anwar:
Systems Bias in Federated Learning. 259-278
Part III
- Nathalie Baracaldo, Runhua Xu
:
Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose? 281-312 - K. R. Jayaram, Ashish Verma:
Private Parameter Aggregation for Federated Learning. 313-336 - Xiao Jin, Pin-Yu Chen, Tianyi Chen:
Data Leakage in Federated Learning. 337-361 - Ambrish Rawat, Giulio Zizzo, Muhammad Zaid Hameed, Luis Muñoz-González:
Security and Robustness in Federated Learning. 363-390 - Yi Zhou, Nathalie Baracaldo, Ali Anwar, Kamala Varma:
Dealing with Byzantine Threats to Neural Networks. 391-414
Part IV
- Runhua Xu
Privacy-Preserving Vertical Federated Learning. 417-438 - Praneeth Vepakomma, Ramesh Raskar:
Split Learning: A Resource Efficient Model and Data Parallel Approach for Distributed Deep Learning. 439-451
Part V
- Toyotaro Suzumura, Yi Zhou, Ryo Kawahara, Nathalie Baracaldo, Heiko Ludwig:
Federated Learning for Collaborative Financial Crimes Detection. 455-466 - Pengqian Yu, Laura Wynter, Shiau Hong Lim:
Federated Reinforcement Learning for Portfolio Management. 467-482 - Ehsan Degan, Shafiq Abedin, David Beymer, Angshuman Deb, Nathaniel Braman, Benedikt Graf, Vandana V. Mukherjee:
Application of Federated Learning in Medical Imaging. 483-497 - Amogh Kamat Tarcar:
Advancing Healthcare Solutions with Federated Learning. 499-508 - Tuan M. Hoang Trong, Mudhakar Srivatsa, Dinesh C. Verma:
A Privacy-preserving Product Recommender System. 509-522 - Utpal Mangla:
Application of Federated Learning in Telecommunications and Edge Computing. 523-534
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-11-15 19:38 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: