Academic

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

arXiv:2604.06631v1 Announce Type: new 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 custom

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Zheng Jiang, Nan He, Yiming Chen, Lifeng Sun · · 1 min read · 11 views
#cs.LG #cs.AI #cs.CV

arXiv:2604.06631v1 Announce Type: new 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.

Executive Summary

SubFLOT addresses critical challenges in Federated Learning (FL) concerning system and statistical heterogeneity by proposing a novel server-side personalized federated pruning framework. It tackles the dilemma of existing pruning methods by introducing an Optimal Transport-enhanced Pruning (OTP) module, which leverages historical client models to infer local data distributions and generate customized submodels without direct data access. Simultaneously, the Scaling-based Adaptive Regularization (SAR) module mitigates parametric divergence among submodels by adaptively penalizing deviations from the global model, scaled by the client's pruning rate. SubFLOT demonstrates superior performance over state-of-the-art methods, promising efficient and personalized model deployment on resource-constrained edge devices.

Key Points

  • ▸ SubFLOT proposes a server-side personalized federated pruning framework.
  • ▸ Optimal Transport-enhanced Pruning (OTP) uses historical client models to infer local data distributions for customized submodel generation.
  • ▸ Scaling-based Adaptive Regularization (SAR) mitigates parametric divergence by adaptively penalizing submodel deviation from the global model, scaled by pruning rate.
  • ▸ The framework aims to overcome the limitations of existing federated pruning methods regarding personalization and computational cost.
  • ▸ Experimental results indicate substantial outperformance against state-of-the-art methods.

Merits

Novelty in Personalization

The use of Optimal Transport (OT) on historical client models for inferring local data distributions to achieve server-side personalization without raw data access is a significant conceptual advancement, bridging a critical gap in FL research.

Addressing Parametric Divergence

The Scaling-based Adaptive Regularization (SAR) module directly confronts the destabilizing effect of parametric divergence, a common pitfall in federated pruning, by introducing a dynamic and client-specific regularization.

Computational Efficiency

By enabling server-side pruning, SubFLOT avoids the computational burden on resource-constrained client devices, making personalized FL more practical for edge deployments.

Comprehensive Problem Formulation

The article addresses both the personalization (via OTP) and stability (via SAR) aspects of federated pruning within a unified framework, offering a holistic solution.

Demerits

Reliance on Historical Models

The effectiveness of OTP heavily depends on the representativeness and quality of historical client models, which might be challenging in highly dynamic or non-stationary data environments.

Interpretability of Optimal Transport

While mathematically sound, the 'treatment' of historical models as proxies for data distributions via Wasserstein distance minimization may lack direct interpretability regarding the specific features or patterns being prioritized for pruning.

Hyperparameter Sensitivity

The balance between the OTP and SAR modules, as well as the scaling factor in SAR, likely introduces new hyperparameters that could require careful tuning for optimal performance across diverse datasets and network architectures.

Scalability with Client Numbers

While server-side, the computational cost of performing optimal transport across numerous historical client models could still become significant as the number of clients scales up, especially for complex model architectures.

Expert Commentary

SubFLOT represents a sophisticated stride in federated learning, ingeniously navigating the tension between personalization, computational feasibility, and model stability. The core innovation lies in the 'Optimal Transport-enhanced Pruning' (OTP) module, which transcends naive pruning by inferring nuanced client-specific model requirements from historical interactions. This move is particularly astute, as it sidesteps the formidable privacy and computational hurdles of client-side data access. Furthermore, the 'Scaling-based Adaptive Regularization' (SAR) module demonstrates a mature understanding of FL dynamics, proactively addressing parametric divergence—a subtle yet critical destabilizer. While the reliance on historical models introduces a dependency that warrants further empirical scrutiny in highly dynamic environments, the architectural elegance and comprehensive problem formulation position SubFLOT as a leading contender in practical federated AI deployment. Its potential to democratize personalized AI on ubiquitous edge devices is profound, offering a blueprint for future research in robust and privacy-aware distributed machine learning.

Recommendations

  • ✓ Conduct extensive ablation studies to thoroughly analyze the individual contributions of the OTP and SAR modules, and their synergistic effects, across diverse datasets and architectures.
  • ✓ Investigate the long-term stability and performance of SubFLOT in highly non-stationary data environments, where historical models might quickly become outdated, and explore mechanisms for adaptive historical model weighting or selection.
  • ✓ Explore methods to enhance the interpretability of the Optimal Transport mechanism in OTP, perhaps by visualizing the feature or layer importance derived from the Wasserstein distance minimization.
  • ✓ Benchmark the computational overhead of the Optimal Transport module on the server side with varying numbers of clients and model complexities, to ensure scalability for large-scale FL deployments.
  • ✓ Consider integrating differential privacy mechanisms or secure aggregation protocols directly into the SubFLOT framework to provide stronger, provable privacy guarantees, particularly concerning the historical model data used by OTP.

Sources

Original: arXiv - cs.LG

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