Preserving Data Privacy via Federated Learning: Challenges and Solutions
By Zengpeng Li, Vishal Sharma, S. Mohanty. 2020
194 citations recorded by the source corpus.
Data have always been a major priority for businesses of all sizes. Businesses tend to enhance their ability in contextualizing data and draw new insights from it as the data itself proliferates with the advancement of technologies. Federated learning acts as a special form of privacy-preserving machine learning technique and can contextualize the data. It is a decentralized training approach for privately collecting and training the data provided by mobile devices, which are located at different geographical locations. Furthermore, users can benefit from obtaining a well-trained machine learning model without sending their privacy-sensitive personal data to the cloud. This article focuses on the most significant challenges associated with the preservation of data privacy via federated learning. Valuable attack mechanisms are discussed, and associated solutions are highlighted to the corresponding attack. Several research aspects along with promising future directions and applications via federated learning are additionally discussed.
Subfields: Artificial Intelligence, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Aruna Prasad Seneviratne
- Nguyen H. Tran
- Jun Li
- Ming Ding
- Jiankun Hu
- Dinh C. Nguyen
- Tuan Dung Nguyen
- Yanming Zhu
In Sociology and Political Science
Citations in the corpus
- Personalized Federated Learning with Moreau Envelopes (2020, background): “Preserving privacy in FL has been studied in [21–25].”
- A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “None of these three surveys [84, 101, 204] analyzed the privacy issues related to FL.”
- Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “[16] FL and data privacy A brief overview on the data privacy challenges and attacks in FL systems, along with possible solutions for data privacy protection in FL designs.”