  Federated learning (FL) allows model training from local data collected by
edge/mobile devices while preserving data privacy, which has wide applicability
to image and vision applications. A challenge is that client devices in FL
usually have much more limited computation and communication resources compared
to servers in a datacenter. To overcome this challenge, we propose PruneFL -- a
novel FL approach with adaptive and distributed parameter pruning, which adapts
the model size during FL to reduce both communication and computation overhead
and minimize the overall training time, while maintaining a similar accuracy as
the original model. PruneFL includes initial pruning at a selected client and
further pruning as part of the FL process. The model size is adapted during
this process, which includes maximizing the approximate empirical risk
reduction divided by the time of one FL round. Our experiments with various
datasets on edge devices (e.g., Raspberry Pi) show that: (i) we significantly
reduce the training time compared to conventional FL and various other
pruning-based methods; (ii) the pruned model with automatically determined size
converges to an accuracy that is very similar to the original model, and it is
also a lottery ticket of the original model.
