33 research outputs found
Personalized Federated Learning with Multi-branch Architecture
Federated learning (FL) is a decentralized machine learning technique that
enables multiple clients to collaboratively train models without requiring
clients to reveal their raw data to each other. Although traditional FL trains
a single global model with average performance among clients, statistical data
heterogeneity across clients has resulted in the development of personalized FL
(PFL), which trains personalized models with good performance on each client's
data. A key challenge with PFL is how to facilitate clients with similar data
to collaborate more in a situation where each client has data from complex
distribution and cannot determine one another's distribution. In this paper, we
propose a new PFL method (pFedMB) using multi-branch architecture, which
achieves personalization by splitting each layer of a neural network into
multiple branches and assigning client-specific weights to each branch. We also
design an aggregation method to improve the communication efficiency and the
model performance, with which each branch is globally updated with weighted
averaging by client-specific weights assigned to the branch. pFedMB is simple
but effective in facilitating each client to share knowledge with similar
clients by adjusting the weights assigned to each branch. We experimentally
show that pFedMB performs better than the state-of-the-art PFL methods using
the CIFAR10 and CIFAR100 datasets.Comment: Accepted by IJCNN 202