# SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning Source: [SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning]() Collection index: [llms.txt]() Data updated: 2026-01-17T05:00:11.890Z Authors: Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite, Jingyu Liu, Vince Calhoun, Sergey Plis Published: 2024-05-15T02:13:51.000Z ## Abstract In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each round between the clients and the server. On standard benchmarks including CIFAR-10, CIFAR-100, and Tiny-ImageNet, SSFL consistently improves the accuracy sparsity trade off, achieving more than 20\\% relative error reduction on CIFAR-10 compared to the strongest sparse baseline, while reducing communication costs by $2 \\times$ relative to dense FL. Finally, in a real-world federated learning deployment, SSFL delivers over $2.3 \\times$ faster communication time, underscoring its practical efficiency. ## 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. ## Original sources and implementation [Original paper]() [PDF]()