The update strategy defines how work is performed locally, on the device.
FELES provides different update strategies in order to realize the federated algorithms:
| name | details |
|---|---|
static |
Static |
bestrt |
Best RT Optimizer |
uniform |
Uniform Optimizer |
equal_computation_time |
Equal Computation Time Optimizer |
The Global Update Optimizer computes epochs (E), batch_size (B) and num_examples (N) so the
amount of computation performed locally by each devices. The configuration can be different for every elected device.
The Global Update Optimizer must implement the optimize method that given a round and a device index return the update configuration, as a dict:
def optimize(self, r: int, dev_index: int, phase: str) -> dict:
Example of returned configuration:
{"epochs": self.epochs, "batch_size": self.batch_size, "num_examples": self.num_examples}
The Static Optimizer statically set E, B and N from the given parameters.
The Best RT Optimizer sets B and N from the given parameters and E is assigned proportionally with respect to the maximum IPS value. The device with the highest IPS (which has the lowest computation) is assigned with the highest number of epochs which is the parameter in the configuration file.
The Uniform Optimizer generates E, B, N with a uniform distribution in the range specified.
The Equal Computation Time Optimizer generates E, B, N depending on a fixed amount of desired computation time and the specific ips value for the device. The assumption is that ips is known for each device.