← 研踪 PaperTrailarXiv:2603.15507 · v2
Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference
首次提交 2026-03-17 00:35
版本更新 2026-11-10 10:29
雷达观测 2026-11-11 11:04
摘要
Federated Learning (FL) enables distributed clients to jointly train a global model without sharing their raw data. However, deploying the resulting deep neural networks on resource-constrained edge devices remains challenging due to their inference-time computational and memory requirements. Binary Neural Networks (BNNs) offer substantial efficiency gains by representing weights and activations using a single bit, but achieving competitive accuracy under such aggressive quantization remains difficult. Rotation-aware binarization reduces quantization errors by aligning real-valued weights with their binary representations. Despite its effectiveness in centralized learning, applying rotation-aware binarization in FL is non-trivial due to discrepancies between local and global models, particularly under heterogeneous client data distributions. To address this challenge, we propose FedBNN, a federated rotation-aware binarization framework that leverages global model information to guide local binarization while incorporating locally learned rotation information into server-side construction of the global binary model. Experiments across multiple benchmark datasets, model architectures, and degrees of data heterogeneity demonstrate that the proposed integrated methodology achieves accuracy competitive with full-precision federated models while generally outperforming existing federated binary learning approaches. Overall, FedBNN enables accurate binary inference in heterogeneous federated settings, with savings of up to $58\times$ in FLOPs and $32\times$ in memory over full-precision models, offering a practical balance between predictive performance and deployment efficiency on resource-constrained edge devices.
作者备注：31 pages, 13 figures
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v2 · 2026-11-10 10:29 · 最新观测版本
Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference
Federated Learning (FL) enables distributed clients to jointly train a global model without sharing their raw data. However, deploying the resulting deep neural networks on resource-constrained edge devices remains challenging due to their inference-time computational and memory requirements. Binary Neural Networks (BNNs) offer substantial efficiency gains by representing weights and activations using a single bit, but achieving competitive accuracy under such aggressive quantization remains difficult. Rotation-aware binarization reduces quantization errors by aligning real-valued weights with their binary representations. Despite its effectiveness in centralized learning, applying rotation-aware binarization in FL is non-trivial due to discrepancies between local and global models, particularly under heterogeneous client data distributions. To address this challenge, we propose FedBNN, a federated rotation-aware binarization framework that leverages global model information to guide local binarization while incorporating locally learned rotation information into server-side construction of the global binary model. Experiments across multiple benchmark datasets, model architectures, and degrees of data heterogeneity demonstrate that the proposed integrated methodology achieves accuracy competitive with full-precision federated models while generally outperforming existing federated binary learning approaches. Overall, FedBNN enables accurate binary inference in heterogeneous federated settings, with savings of up to $58\times$ in FLOPs and $32\times$ in memory over full-precision models, offering a practical balance between predictive performance and deployment efficiency on resource-constrained edge devices.
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