  Federated learning enables multiple distributed devices to collaboratively
learn a shared prediction model without centralizing their on-device data. Most
of the current algorithms require comparable individual efforts for local
training with the same structure and size of on-device models, which, however,
impedes participation from resource-constrained devices. Given the widespread
yet heterogeneous devices nowadays, in this paper, we propose an innovative
federated learning framework with heterogeneous on-device models through
Zero-shot Knowledge Transfer, named by FedZKT. Specifically, FedZKT allows
devices to independently determine the on-device models upon their local
resources. To achieve knowledge transfer across these heterogeneous on-device
models, a zero-shot distillation approach is designed without any prerequisites
for private on-device data, which is contrary to certain prior research based
on a public dataset or a pre-trained data generator. Moreover, this
compute-intensive distillation task is assigned to the server to allow the
participation of resource-constrained devices, where a generator is
adversarially learned with the ensemble of collected on-device models. The
distilled central knowledge is then sent back in the form of the corresponding
on-device model parameters, which can be easily absorbed on the device side.
Extensive experimental studies demonstrate the effectiveness and robustness of
FedZKT towards on-device knowledge agnostic, on-device model heterogeneity, and
other challenging federated learning scenarios, such as heterogeneous on-device
data and straggler effects.
