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 $58times$ in FLOPs and $32times$ in memory over full-precision models, offering a practical balance between predictive performance and deployment efficiency on resource-constrained edge devices.
Source: arXiv cs.CV (Vision) — Read the full article on the original site ↗