Training Keyword Spotting Models on Non-IID Data with Federated Learning
Table 1: Comparisons of offline false accept and reject rates for various optimizers on non-IID data.
['Optimizer', 'FA [%]', 'FR [%]']
Exponentially decayed client learning rates were used with FedAdam and FedYogi, while FedAvg worked better with constant client learning rates. The adaptive optimizers had a decisive advantage over FedAvg on FR. Replacing classical momentum with NAG benefitted FedAvg and FedAdam, but FedYogi with classical momentum had the lowest FR overall.