Neuron Personalization of Collaborative Federated Learning via Device-to-Device Communications

Wireless Ad Hoc Federated Learning (WAFL) has been proposed to allow fully distributed collaborative learning in a device-to-device communication without depending on any centralized mechanisms. The main focus of previous studies has been to generalize models of users over label distribution skew cases in not independent and identically distributed (Non- IID) scenarios. However, a generalized WAFL model does not always provide correct answers to each individual over label preference skew, which needs personalization. We proposed a personalization method of WAFL for label preference skew in a previous work, which divide a local model into public and private parameters by layer. In this paper, we propose more fine- grained parameter decoupling approach, Neuron Personalization. We have carried out evaluations using modified CIFAR10 dataset and Pascal VOC dataset. The results indicate that the Neuron Personalization have a good effects to keeping the balance between generalization and personalization for label preference skew.