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arXiv · 2405.09037

SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning

Riyasat Ohib·Bishal Thapaliya·Gintare Karolina Dziugaite·Jingyu Liu·Vince Calhoun·Sergey Plis
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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.

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Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite, Jingyu Liu, Vince Calhoun, Sergey Plis. 2024-05-15. SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning. https://arxiv.org/abs/2405.09037

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