Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes

• URL: http://arxiv.org/abs/2501.08521v1
• Date: Wed, 15 Jan 2025 02:17:38 GMT
• Title: Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes
• Authors: Huy Q. Le, Ye Lin Tun, Yu Qiao, Minh N. H. Nguyen, Keon Oh Kim, Choong Seon Hong,
• Abstract summary: We introduce a novel federated prototype learning method, which incorporates $textbfI$ntra-domain and $textbfI$nter-domain. We propose feature alignment with MixUp-based augmented prototypes to capture the diversity of local domains and enhance the generalization of local features.
• Score: 28.507105940018704
• License:
• Abstract: Federated Learning (FL) has emerged as a decentralized machine learning technique, allowing clients to train a global model collaboratively without sharing private data. However, most FL studies ignore the crucial challenge of heterogeneous domains where each client has a distinct feature distribution, which is common in real-world scenarios. Prototype learning, which leverages the mean feature vectors within the same classes, has become a prominent solution for federated learning under domain skew. However, existing federated prototype learning methods only consider inter-domain prototypes on the server and overlook intra-domain characteristics. In this work, we introduce a novel federated prototype learning method, namely I$^2$PFL, which incorporates $\textbf{I}$ntra-domain and $\textbf{I}$nter-domain $\textbf{P}$rototypes, to mitigate domain shifts and learn a generalized global model across multiple domains in federated learning. To construct intra-domain prototypes, we propose feature alignment with MixUp-based augmented prototypes to capture the diversity of local domains and enhance the generalization of local features. Additionally, we introduce a reweighting mechanism for inter-domain prototypes to generate generalized prototypes to provide inter-domain knowledge and reduce domain skew across multiple clients. Extensive experiments on the Digits, Office-10, and PACS datasets illustrate the superior performance of our method compared to other baselines.

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