FedDGIC: Reliable and Efficient Asynchronous Federated Learning with Gradient Compensation.
Zaipeng XieJunchen JiangRuifeng ChenZhihao QuHanxiang LiuPublished in: ICPADS (2022)
Keyphrases
- neural network
- learning algorithm
- supervised learning
- computationally efficient
- unsupervised learning
- knowledge acquisition
- data sets
- cost effective
- learning process
- digital libraries
- e learning
- artificial intelligence
- support vector
- active learning
- multi agent
- database systems
- empirical studies
- learning systems
- mobile learning
- real time
- learning tasks
- learning scenarios
- inductive learning
- learning analytics