SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport

• URL: http://arxiv.org/abs/2604.06631v1
• Date: Wed, 08 Apr 2026 03:13:10 GMT
• Title: SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
• Abstract summary: Federated Learning (FL) enables collaborative model training while preserving data privacy.<n>Current methods face a critical dilemma: server-side pruning lacks personalization, whereas client-side pruning is computationally prohibitive for resource-constrained devices.<n>We propose SubFLOT, a novel framework for server-side personalized federated pruning.
• Score: 13.848917214804198
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Federated Learning (FL) enables collaborative model training while preserving data privacy, but its practical deployment is hampered by system and statistical heterogeneity. While federated network pruning offers a path to mitigate these issues, existing methods face a critical dilemma: server-side pruning lacks personalization, whereas client-side pruning is computationally prohibitive for resource-constrained devices. Furthermore, the pruning process itself induces significant parametric divergence among heterogeneous submodels, destabilizing training and hindering global convergence. To address these challenges, we propose SubFLOT, a novel framework for server-side personalized federated pruning. SubFLOT introduces an Optimal Transport-enhanced Pruning (OTP) module that treats historical client models as proxies for local data distributions, formulating the pruning task as a Wasserstein distance minimization problem to generate customized submodels without accessing raw data. Concurrently, to counteract parametric divergence, our Scaling-based Adaptive Regularization (SAR) module adaptively penalizes a submodel's deviation from the global model, with the penalty's strength scaled by the client's pruning rate. Comprehensive experiments demonstrate that SubFLOT consistently and substantially outperforms state-of-the-art methods, underscoring its potential for deploying efficient and personalized models on resource-constrained edge devices.

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