Journal
KNOWLEDGE-BASED SYSTEMS
Volume 216, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2021.106775
Keywords
Federated learning; Privacy protection; Machine learning
Categories
Funding
- Key Research and Development Program of Shaanxi Province, China [2019ZDLGY17-01, 2019GY-042]
- Fundamental Research Funds for the Central Universities, China
- Innovation Fund of Xidian University, China
Ask authors/readers for more resources
Federated learning is a setup where multiple clients collaborate to solve machine learning problems under the coordination of a central aggregator. It reduces systematic privacy risks and costs through local computing and model transmission. This method ensures data privacy for each device and improves learning efficiency and security.
Federated learning is a set-up in which multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator. This setting also allows the training data decentralized to ensure the data privacy of each device. Federated learning adheres to two major ideas: local computing and model transmission, which reduces some systematic privacy risks and costs brought by traditional centralized machine learning methods. The original data of the client is stored locally and cannot be exchanged or migrated. With the application of federated learning, each device uses local data for local training, then uploads the model to the server for aggregation, and finally the server sends the model update to the participants to achieve the learning goal. To provide a comprehensive survey and facilitate the potential research of this area, we systematically introduce the existing works of federated learning from five aspects: data partitioning, privacy mechanism, machine learning model, communication architecture and systems heterogeneity. Then, we sort out the current challenges and future research directions of federated learning. Finally, we summarize the characteristics of existing federated learning, and analyze the current practical application of federated learning. (C) 2021 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available