Model Pruning Enables Efficient Federated Learning on Edge Devices

2 code implementations • 26 Sep 2019 • Yuang Jiang, Shiqiang Wang, Victor Valls, Bong Jun Ko, Wei-Han Lee, Kin K. Leung, Leandros Tassiulas

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.

Federated Learning