| 1 Function | 2 Reference Model | 3 Input/Output Data |
| 4 SubAIMs | 5 JSON Metadata | 6 Profiles |
| 7 Reference Software | 8 Conformance Texting | 9 Performance Assessment |
1 Functions
The Health Federated Learning (AIH-HFL) AIM:
| Receives | Federated Learning Response | Response to Federated Learning Request (NN Model) |
| Produces | Neural Network Model | NN Model submitted to MPAI Store |
| Federated Learning Request | Request to Health Front End for a given NN Model |
2 Reference Model
The Health Federated Learning (AIH-HFL) AIM Reference Model is depicted in Figure 1.
Figure 1 – The Health Federated Learning (AIH-HFL) AIM Reference Model
3 Input/Output Data
Table 1 specifies the Input and Output Data of the The Health Federated Learning (AIH-HFL) AIM. Links are to the Data Type specifications.
Table 1 – I/O Data of theThe Health Federated Learning (AIH-HFL) AIM
| Input | Description |
| Federated Learn Response | Response to Federated Learning Request (NN Model). |
| Output | Description |
| NN Model | NN Model submitted to MPAI Store. |
| Federated Learn Request | Request to Health Front End for a given NN Model. |
4 SubAIMs
No SubAIMs
5 JSON Metadata
https://schemas.mpai.community/AIH1/V1.0/AIMs/HealthFederatedLearning.json
6 Profiles
No Profiles
7 Reference Software
Under development.
8 Conformance Testing
Table 2 provides the Conformance Testing Method for Health Federated Learning (AIH-HFL) AIM.
If a schema contains references to other schemas, conformance of data for the primary schema implies that any data referencing a secondary schema shall also validate against the relevant schema, if present and conform with the Qualifier, if present.
Table 2 – Conformance Testing Method for OSD-EVD AIM
| Receives | Federated Learn Response | Shall validate against Federated Learning schema. |
| Produces | NN Model | Shall validate against ML Model schema. |
| Federated Learn Request | Shall validate against Federated Learning schema. |
9 Performance Assessment
