Model Pruning Enables Efficient Federated Learning on Edge Devices

Abstract Federated learning (FL) allows model training from local data collected by edge/mobile devices, while preserving data privacy. 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 converges to an accuracy that is very similar to the original model but has a much smaller size, and it is also a lottery ticket of the original model.
  • Yuang Jiang (Yale)
  • Shiqiang Wang (IBM US)
  • Victor Valls (Yale)
  • Bong Jun Ko (IBM US)
  • Wei-Han Lee (IBM US)
  • Kin K. Leung (Imperial)
  • Leandros Tassiulas (Yale)
Date Dec-2020
Venue Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL) in Conjunction with NeurIPS 2020