FedMef: Towards Memory-efficient Federated Dynamic Pruning
This work addresses memory efficiency for federated learning on resource-constrained devices, representing an incremental improvement over existing methods.
The paper tackles the challenge of high memory usage in federated learning on resource-constrained devices by proposing FedMef, a memory-efficient federated dynamic pruning framework, which reduces memory footprint by 28.5% compared to state-of-the-art methods while achieving superior accuracy.
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 substantial challenges, including post-pruning performance degradation, 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 footprints, which is particularly beneficial for deploying FL to memory-limited devices. Extensive experiments demonstrate 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.