An Agent-based Federated Learning Object Search Service

By Carla Fillmann Barcelos, João Carlos Gluz, R. Vicari. 2011

27 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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

1. Dianbo Liu
2. Li Huang
3. Yifeng Yin
4. Zeng Fu
5. Hao Deng
6. Shifa Zhang
7. Benjamin S. Glicksberg
8. Chang Su

In Computer Networks and Communications

1. Fei Wang

Citations in the corpus

• LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data (2018, background): “[2] created an agent-based federated catalog of learning objects (AgCAT system) to facilitate assess of distributed educational resources.”
• LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data (2018, background): “∑ t ∑k ( E 2 + retraining epochs for Client k at tth global round) T ·m (2)”
• Federated Learning for Healthcare Informatics (2019, background): “Subsequently, there were several efforts aiming at building federations of learning content and content repositories [6, 74, 83].”