FedMef

Hong Huang, Weiming Zhuang, Chen Chen, Lingjuan Lyu

Abstract: Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However, its application on resource-constrained devices is challenging due to the high demand for computation and memory resources to train deep learning models. Neural network pruning techniques, such as dynamic pruning, could enhance model efficiency, but directly adopting them in FL still poses substan-tial challenges, including post-pruning performance degra-dation, high activation memory usage, etc. To address these challenges, we propose FedMef, a novel and memory-efficient federated dynamic pruning framework. FedMef comprises two key components. First, we introduce the budget-aware extrusion that maintains pruning efficiency while preserving post-pruning performance by salvaging crucial information from parameters marked for pruning within a given budget. Second, we propose scaled activation pruning to effectively reduce activation memory foot-prints, which is particularly beneficial for deploying FL to memory-limited devices. Extensive experiments demon-strate the effectiveness of our proposed FedMef In particular, it achieves a significant reduction of 28.5% in memory footprint compared to state-of-the-art methods while obtaining superior accuracy. © 2024 IEEE.

External IDs:doi:10.1109/cvpr52733.2024.02601