This performance relies heavily on the configuration of the network parameters.
In this work, we introduce the node copying model for constructing a distribution over graphs.
Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression.
Structured pruning is an effective compression technique to reduce the computation of neural networks, which is usually achieved by adding perturbations to reduce network parameters at the cost of slightly increasing training loss.
By incorporating an approximated L1-norm and the correlation between client models and global model into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with non-sparse FL.
Our simulation result show that the proposed adjustment significantly improves the performance of the causal direction test statistic for heterogeneous data.
The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model.