Semi-Federated Learning
Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less up-link transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.
Code (1)
Tasks
ClusteringFederated LearningSimilar Papers 제목 키워드 기반
Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks
Implementing existing federated learning in massive Internet of Things (IoT) networks faces critical challenges such as imbalanced and statistically heterogeneous data and device diversity. To this end, we propose a semi…
DiversityFederated LearningFedParsing: a Semi-Supervised Federated Learning Model on Semantic Parsing
Although many semantic parsing models have been proven to work effectively on "NL-to-SQL", the limitation of annotated datasets remains a great challenge. In many semi-supervised models, while they use unlabeled data to …
Federated LearningSemantic ParsingFederated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
While existing federated learning approaches mostly require that clients have fully-labeled data to train on, in realistic settings, data obtained at the client-side often comes without any accompanying labels. Such defi…
Federated LearningVertical Semi-Federated Learning for Efficient Online Advertising
The traditional vertical federated learning schema suffers from two main issues: 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving, which limits its applicat…
Federated LearningVertical Federated LearningSemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling
Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy. However, most recent research and applicat…
Data AugmentationFederated LearningPseudo Label