Privacy and Robustness in Federated Learning: Attacks and Defenses
Sony Computer Science Laboratories · Nanyang Technological University · +6 more institutions
Abstract
As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continues to thrive in this new reality. Existing FL protocol designs have been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this article, we conduct a…
Citation impact
- FWCI
- 54.10
- Percentile
- 100%
- References
- 316
Authors
8Topics & keywords
- Robustness (evolution)
- Computer science
- Computer security
- Multidisciplinary approach
- Federated learning
- Information privacy
- Privacy by Design
- Internet privacy
- Peace, Justice and strong institutions