Personalized Federated Learning With Structure
- URL: http://arxiv.org/abs/2203.00829v3
- Date: Fri, 4 Mar 2022 07:49:04 GMT
- Title: Personalized Federated Learning With Structure
- Authors: Fengwen Chen, Guodong Longr, Zonghan Wu, Tianyi Zhou and Jing Jiang
- Abstract summary: We propose a novel structured federated learning(SFL) framework to simultaneously learn the global model and personalized model.
In contrast to a pre-defined structure, our framework could be further enhanced by adding a structure learning component to automatically learn the structure.
- Score: 24.566947384179837
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowledge sharing and model personalization are two key components to impact
the performance of personalized federated learning (PFL). Existing PFL methods
simply treat knowledge sharing as an aggregation of all clients regardless of
the hidden relations among them. This paper is to enhance the knowledge-sharing
process in PFL by leveraging the structural information among clients. We
propose a novel structured federated learning(SFL) framework to simultaneously
learn the global model and personalized model using each client's local
relations with others and its private dataset. This proposed framework has been
formulated to a new optimization problem to model the complex relationship
among personalized models and structural topology information into a unified
framework. Moreover, in contrast to a pre-defined structure, our framework
could be further enhanced by adding a structure learning component to
automatically learn the structure using the similarities between clients'
models' parameters. By conducting extensive experiments, we first demonstrate
how federated learning can be benefited by introducing structural information
into the server aggregation process with a real-world dataset, and then the
effectiveness of the proposed method has been demonstrated in varying degrees
of data non-iid settings.
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