  Federated Learning (FL) enables collaborative learning of large-scale
distributed clients without data sharing. However, due to the disparity of
computing resources among massive mobile computing devices, the performance of
traditional homogeneous model-based Federated Learning (FL) is seriously
limited. On the one hand, to achieve model training in all the diverse clients,
mobile computing systems can only use small low-performance models for
collaborative learning. On the other hand, devices with high computing
resources cannot train a high-performance large model with their insufficient
raw data. To address the resource-constrained problem in mobile computing
systems, we present a novel heterogeneous FL approach named AdapterFL, which
uses a model reassemble strategy to facilitate collaborative training of
massive heterogeneous mobile devices adaptively. Specifically, we select
multiple candidate heterogeneous models based on the computing performance of
massive mobile devices and then divide each heterogeneous model into two
partitions. By reassembling the partitions, we can generate models with varied
sizes that are combined by the partial parameters of the large model with the
partial parameters of the small model. Using these reassembled models for FL
training, we can train the partial parameters of the large model using
low-performance devices. In this way, we can alleviate performance degradation
in large models due to resource constraints. The experimental results show that
AdapterFL can achieve up to 12\% accuracy improvement compared to the
state-of-the-art heterogeneous federated learning methods in
resource-constrained scenarios.
