Federated Learning with Position-Aware Neurons [article]

Xin-Chun Li and Yi-Chu Xu and Shaoming Song and Bingshuai Li and Yinchuan Li and Yunfeng Shao and De-Chuan Zhan
2022 arXiv   pre-print
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)
more » ... nto 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.
arXiv:2203.14666v2 fatcat:eonoc2imazci7lbvhzbffkq6tq