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flgo.simulator

This module is to simulate arbitrary system heterogeneity that may occur in practice. We conclude four types of system heterogeneity from existing works.

System Heterogeneity Description

  1. Availability: the devices will be either available or unavailable at each moment, where only the available devices can be selected to participate in training.

  2. Responsiveness: the responsiveness describes the length of the period from the server broadcasting the gloabl model to the server receiving the locally trained model from a particular client.

  3. Completeness: since the server cannot fully control the behavior of devices,it's possible for devices to upload imcomplete model updates (i.e. only training for a few steps).

  4. Connectivity: the clients who promise to complete training may suffer accidients so that the server may lose connections with these client who will never return the currently trained local_movielens_recommendation model.

We build up a client state machine to simulate the four types of system heterogeneity, and provide high-level APIs to allow customized system heterogeneity simulation.

Example: How to customize the system heterogeneity:

>>> class MySimulator(flgo.simulator.base.BasicSimulator):
...     def update_client_availability(self):
...         # update the variable 'prob_available' and 'prob_unavailable' for all the clients
...         self.set_variable(self.all_clients, 'prob_available', [0.9 for _ in self.all_clients])
...         self.set_variable(self.all_clients, 'prob_unavailable', [0.1 for _ in self.all_clients])
...
...     def update_client_connectivity(self, client_ids):
...         # update the variable 'prob_drop' for clients in client_ids
...         self.set_variable(client_ids, 'prob_drop', [0.1 for _ in client_ids])
...
...     def update_client_responsiveness(self, client_ids, *args, **kwargs):
...         # update the variable 'latency' for clients in client_ids
...         self.set_variable(client_ids, 'latency', [np.random.randint(5,100) for _ in client_ids])
...
...     def update_client_completeness(self, client_ids, *args, **kwargs):
...         # update the variable 'working_amount' for clients in client_ids
...         self.set_variable(client_ids, 'working_amount',  [max(int(self.clients[cid].num_steps*np.random.rand()), 1) for cid in client_ids])
>>> r = flgo.init(task, algorithm=fedavg, Simulator=MySimulator)
>>> # The runner r will be runned under the customized system heterogeneity, where the clients' states will be flushed by
>>> # MySimulator.update_client_xxx at each moment of the virtual clock or particular events happen (i.e. a client was selected)

We also provide some preset Simulator like flgo.simulator.DefaultSimulator and flgo.simulator.