A novel parameter decoupling approach of personalised federated learning for image analysis

Abstract

Given the importance of privacy protection in databases and other institutions, federated learning (FL) is used to benefit training machine learning models based on these decentralised and private data so as to address the growing vision tasks. However, for federated learning, statistical heterogeneity continues to be a major problem. Recently, plenty of personalised federated learning methods have been explored to solve the problem of statistical heterogeneity. The usage of trained base layers and the effect of feature extraction in personalised layers, however, are hardly considered in those methods that employ the learning personalised models approach. To address the problem of the statistical heterogeneity in image analysis, PCCFED, a personalised federated learning method utilizing the strategy of parameter decoupling is proposed. It should be emphasised that the authors’ personalised federated learning method decouples the personalised (P) layers into a connecting (C) layer and classifier (C) layer in order to enhance the effectiveness of feature learning for personalised layers. Further, an approach is proposed to fully use the base layers to adapt a personalised model based on the newly admitted institution's dataset through meta‐transfer. The performance of the proposed PCCFED on three datasets is evaluated under the practical non‐independent and identically distributed (non‐IID) setting. Extensive experiments demonstrate that compared with baseline methods, the proposed framework achieves the best performance in federated learning and fine‐tuning. Through FL, the investigation reveals a method to reduce statistical heterogeneity while protecting the institutions' privacy.

Personalized Fair Split Learning for Resource-Constrained Internet of Things

“…Architecture-based methods focus on providing tailored and personalized model architectures for individual clients. The approach can be implemented using techniques such as parameter decoupling [ 27 ] and knowledge distillation [ 28 ], where parameter decoupling mainly provides a personalization layer for each client.…”