Figure 2.

Federated implementation

(A) Visualization of data structure. The data have a panel structure with many observations per individual and varying treatment timing.

(B) High-level algorithm of the CSDID. For each combination of evaluation period and treatment period, an ATT and its standard error are computed using the influence function of each individual.

(C) CSDID for central learning. In central learning, the data are all at one server such that the sample analogs can be computed directly.

(D) CSDID for federated learning. The analysis is initialized by using functions from the client side package dsDidClient that calls the dsDid package on the server sides using opal. During the local computations of the influence functions on the server sides, security checks are enforced in order to guarantee data privacy. The server-side influences are aggregated on each server and only the aggregated information is sent to the client side at which ATTs and standard errors are computed. This figure has been designed using resources from Flaticon.com.