Model Pruning Enables Efficient Federated Learning on Edge Devices

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 data center. 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…

• NS
  National Science Foundation

  Award: CNS-2112562
• OO
  Office of Naval Research

  Award: N00014-19-1-2566
• AR
  Army Research Laboratory

  Award: W911NF-16-3-0001