hetero_kmeans.md 1.9 KB
Federated Kmeans
Kmeans is a simple statistic model widely used for clustering. FATE provides Heterogeneous Kmeans(HeteroKmeans).
Here we simplify participants of the federation process into three parties. Party A represents Guest, party B represents Host. Party C, which is also known as “Arbiter,” is a third party that works as a coordinator. Party C is responsible for generating private and public keys.
Heterogeneous Kmeans
The process of HeteroKmeans training is shown below:
Figure 1 (Federated HeteroKmeans Principle)
A sample alignment process is conducted before training. The sample alignment process identifies overlapping samples in databases of all parties. The federated model is built based on the overlapping samples. The whole sample alignment process is conducted in encryption mode, and so confidential information (e.g. sample ids) will not be leaked.
In the training process, party A will choose centroids from samples randomly and send them to party B . Party A and party B then compute the distance to centroids ,which is needed for label assignment. Arbiter aggregates, calculates, and returns back the final label to each sample and repeats this part until the max iter or tolerance meets the criteria.
During the aggregate process, parties will use secure aggregate as all sent distances will be added with random numbers that can be combined to zero when aggregating at arbiter.
Features
- Tolerance & Max_iter supported for convergence
- Random_stat specify supported
- Centroids are selected randomly
- Labeled and unlabeled data supported