论文标题
使用位置感知神经元的联合学习
Federated Learning with Position-Aware Neurons
论文作者
论文摘要
联合学习（FL）融合了来自本地节点的协作模型，而无需集中用户的数据。神经网络和非i.i.d的置换不变属性。跨客户端的数据使本地更新的参数不恰当地对齐，从而禁用基于坐标的参数平均。传统神经元不会明确考虑位置信息。因此，我们建议将位置感知的神经元（PAN）作为替代的，融合位置相关的值（即位置编码）到神经元输出中。 PANS将自己置于自己的位置，并最大程度地减少脱位的可能性，甚至可以更新异质数据。我们打开/关闭平底锅以禁用/启用神经网络的排列不变属性。当应用于FL时，PANS与位置紧密相结合，从而使跨客户的参数预先对齐并促进基于坐标的参数平均。 PANS是算法 - 敏锐的，可以普遍改善现有的FL算法。此外，“ fl with pans”非常易于实现，并且在计算上友好。
Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i.d. data across clients make the locally updated parameters imprecisely aligned, disabling the coordinate-based parameter averaging. Traditional neurons do not explicitly consider position information. Hence, we propose Position-Aware Neurons (PANs) as an alternative, fusing position-related values (i.e., position encodings) into neuron outputs. PANs couple themselves to their positions and minimize the possibility of dislocation, even updating on heterogeneous data. We turn on/off PANs to disable/enable the permutation invariance property of neural networks. PANs are tightly coupled with positions when applied to FL, making parameters across clients pre-aligned and facilitating coordinate-based parameter averaging. PANs are algorithm-agnostic and could universally improve existing FL algorithms. Furthermore, "FL with PANs" is simple to implement and computationally friendly.
