Automatic speech recognition models require large amounts of speech
recordings for training. However, the collection of such data often is
cumbersome and leads to privacy concerns. Federated learning has been widely
used as an effective decentralized technique that collaboratively learns a
shared prediction model while keeping the data local on different clients.
Unfortunately, client devices often feature limited computation and
communication resources leading to practical difficulties for large models. In
addition, the heterogeneity that characterizes edge devices makes it
sub-optimal to generate a single model that fits all of them. Differently from
the recent literature, where multiple models with different architectures are
used, in this work, we propose using dynamical architectures which, employing
early-exit solutions, can adapt their processing (i.e. traversed layers)
depending on the input and on the operation conditions. This solution falls in
the realm of partial training methods and brings two benefits: a single model
is used on a variety of devices; federating the models after local training is
straightforward. Experiments on public datasets show that our proposed approach
is effective and can be combined with basic federated learning strategies.

自动语音识别模型需要大量的语音录音进行训练。本文提出使用动态架构和局部训练方法，在各种设备上使用单一模型，并结合基本联邦学习策略，证明了这种方法的有效性。