# Model Pruning Enables Efficient Federated Learning on Edge Devices Source: [Model Pruning Enables Efficient Federated Learning on Edge Devices]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:05:48.516Z Authors: Yuang Jiang, Shiqiang Wang, Victor Valls, Bong Jun Ko, Wei-Han Lee, Kin K. Leung, Leandros Tassiulas Published: 2019-09-26T18:32:33.000Z ## Abstract 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 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 with automatically determined size converges to an accuracy that is very similar to the original model, and it is also a lottery ticket of the original model. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR100 (test) (Alpha-Weighted Accuracy)](): Image Classification; CIFAR100 (test); Accuracy (alpha=0.1): 38.12. - [Image Classification on FMNIST (test) with Alpha-Weighted Accuracy](): Image Classification; fMNIST (test); Accuracy (alpha=0.1): 77.08. - [Image Classification on CIFAR10 (test) (Alpha-Weighted Accuracy)](): Image Classification; CIFAR10 (test); Accuracy (alpha=0.1): 54.48. ## Original sources and implementation [Original paper]() [PDF]()