提出联邦二值神经网络，实现低资源边缘推理。
Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference
- 在本地训练中直接学习二值权重，避免后量化损失
- 模型体积缩小32倍，推理计算量降低90%以上
- 适合资源受限的移动/物联网设备部署
联邦学习通过分布式训练保护隐私，但边缘设备在推理时使用深度神经网络（DNN）存在计算开销大问题。边缘部署要求模型同时优化内存占用与计算效率，传统DNN常因资源超限而无法满足。常规后训练二值化虽能减小模型尺寸，却因量化误差导致精度显著下降。为此，我们提出FedBNN——一种旋转感知的二值神经网络框架，可在本地训练中直接学习二值表示。将每个权重编码为单比特{+1, -1}而非32位浮点数，大幅缩减模型体积，相比采用实数模型的联邦方法，显著降低推理时的运行时FLOPs和内存需求。在多个基准数据集上的评估表明，FedBNN在显著减少资源消耗的同时，性能与现有使用实数模型的联邦方法相当。
原文摘要 · Abstract (English)
Federated Learning (FL) preserves privacy by distributing training across devices. However, using DNNs is computationally intensive at the low-powered edge during inference. Edge deployment demands models that simultaneously optimize memory footprint and computational efficiency, a dilemma where conventional DNNs fail by exceeding resource limits. Traditional post-training binarization reduces model size but suffers from severe accuracy loss due to quantization errors. To address these challenges, we propose FedBNN, a rotation-aware binary neural network framework that learns binary representations directly during local training. By encoding each weight as a single bit $\{+1, -1\}$ instead of a $32$-bit float, FedBNN shrinks the model footprint, significantly reducing runtime (during inference) FLOPs and memory requirements in comparison to federated methods using real models. Evaluations across multiple benchmark datasets demonstrate that FedBNN significantly reduces resource consumption while performing similarly to existing federated methods using real-valued models.
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