Over-the-Air Federated Learning (OTA-FL) has been extensively investigated as
a privacy-preserving distributed learning mechanism. Realistic systems will see
FL clients with diverse size, weight, and power configurations. A critical
research gap in existing OTA-FL research is the assumption of homogeneous
client computational bit precision. Indeed, many clients may exploit
approximate computing (AxC) where bit precisions are adjusted for energy and
computational efficiency. The dynamic distribution of bit precision updates
amongst FL clients poses an open challenge for OTA-FL, as is is incompatible in
the wireless modulation superposition space.
Here, we propose an AxC-based OTA-FL framework of clients with multiple
precisions, demonstrating the following innovations: (i) optimize the
quantization-performance trade-off for both server and clients within the
constraints of varying edge computing capabilities and learning accuracy
requirements, and (ii) develop heterogeneous gradient resolution OTA-FL
modulation schemes to ensure compatibility with physical layer OTA aggregation.
Our findings indicate that we can design modulation schemes that enable AxC
based OTA-FL, which can achieve 50\% faster and smoother server convergence and
a performance enhancement for the lowest precision clients compared to a
homogeneous precision approach. This demonstrates the great potential of our
AxC-based OTA-FL approach in heterogeneous edge computing environments.

我们提出了一种基于近似计算的 OTA-FL 框架，通过优化量化性能平衡、开发异构梯度分辨率 OTA-FL 调制方案，实现了异构边缘计算环境中的 OTA-FL，并能够以 50% 更快和更平滑的服务器收敛速度和性能增强方式，比同质化精度方法更好地满足最低精度客户的要求。

通过近似计算的混合精度无线联邦学习

Mixed-Precision Over-The-Air Federated Learning via Approximated  Computing

This paper presents a novel approach to network pruning, targeting block
pruning in deep neural networks for edge computing environments. Our method
diverges from traditional techniques that utilize proxy metrics, instead
employing a direct block removal strategy to assess the impact on
classification accuracy. This hands-on approach allows for an accurate
evaluation of each block's importance. We conducted extensive experiments on
CIFAR-10, CIFAR-100, and ImageNet datasets using ResNet architectures. Our
results demonstrate the efficacy of our method, particularly on large-scale
datasets like ImageNet with ResNet50, where it excelled in reducing model size
while retaining high accuracy, even when pruning a significant portion of the
network. The findings underscore our method's capability in maintaining an
optimal balance between model size and performance, especially in
resource-constrained edge computing scenarios.

该论文介绍了一种新的网络剪枝方法，针对边缘计算环境中的深度神经网络中的块剪枝。研究方法采用直接的块移除策略来评估对分类准确性的影响，通过对 CIFAR-10、CIFAR-100 和 ImageNet 数据集以 ResNet 架构进行广泛实验，结果表明该方法在减小模型大小的同时保持高准确性方面的有效性，在网络的大部分被剪枝的情况下，尤其是在 ImageNet 上通过对 ResNet50 的剪枝时，其结果突出了该方法在维持模型大小和性能之间的最佳平衡能力，尤其是在资源有限的边缘计算场景中。