Algorithm

In FLGo, each algorithm is described by an independent file consisting of the objects (i.e. server and clients in horizontal FL) with their actions.

Horizontal FL

A classical procedure of FL training process is as shown in the figure above, where the server iteratively broadcasts the global model to a subset of clients and aggregates the received locally trained models from them. Following this scheme, a great number of FL algorithms can be easily implemented by FLGo. For example, to implement methods that customize the local training process (e.g. FedProx, MOON), developers only need to modify the function client.train(...). And a series of sampling strategies can be realized by only replacing the function server.sample(). We also provide comprehensive tutorial for using FLGo to implement the state of the art algorithms. In addition, asynchronous algorithms can share the same scheme with synchronous algorithms in FLGo, where developers only need to concern about the sampling strategy and how to deal with the currently received packages from clients at each moment.

Vertical FL

To be completed.