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FedNSAM:Consistency of Local and Global Flatness for Federated Learning

arXiv:2602.23827v1 Announce Type: new Abstract: In federated learning (FL), multi-step local updates and data heterogeneity usually lead to sharper global minima, which degrades the performance of the global model. Popular FL algorithms integrate sharpness-aware minimization (SAM) into local training to address this issue. However, in the high data heterogeneity setting, the flatness in local training does not imply the flatness of the global model. Therefore, minimizing the sharpness of the local loss surfaces on the client data does not enable the effectiveness of SAM in FL to improve the generalization ability of the global model. We define the \textbf{flatness distance} to explain this phenomenon. By rethinking the SAM in FL and theoretically analyzing the \textbf{flatness distance}, we propose a novel \textbf{FedNSAM} algorithm that accelerates the SAM algorithm by introducing global Nesterov momentum into the local update to harmonize the consistency of global and local flatness

J
Junkang Liu, Fanhua Shang, Yuxuan Tian, Hongying Liu, Yuanyuan Liu · · 1 min read · 12 views
#cs.LG #cs.AI

arXiv:2602.23827v1 Announce Type: new Abstract: In federated learning (FL), multi-step local updates and data heterogeneity usually lead to sharper global minima, which degrades the performance of the global model. Popular FL algorithms integrate sharpness-aware minimization (SAM) into local training to address this issue. However, in the high data heterogeneity setting, the flatness in local training does not imply the flatness of the global model. Therefore, minimizing the sharpness of the local loss surfaces on the client data does not enable the effectiveness of SAM in FL to improve the generalization ability of the global model. We define the \textbf{flatness distance} to explain this phenomenon. By rethinking the SAM in FL and theoretically analyzing the \textbf{flatness distance}, we propose a novel \textbf{FedNSAM} algorithm that accelerates the SAM algorithm by introducing global Nesterov momentum into the local update to harmonize the consistency of global and local flatness. \textbf{FedNSAM} uses the global Nesterov momentum as the direction of local estimation of client global perturbations and extrapolation. Theoretically, we prove a tighter convergence bound than FedSAM by Nesterov extrapolation. Empirically, we conduct comprehensive experiments on CNN and Transformer models to verify the superior performance and efficiency of \textbf{FedNSAM}. The code is available at https://github.com/junkangLiu0/FedNSAM.

Executive Summary

The article introduces FedNSAM, a novel algorithm that enhances the consistency of local and global flatness in federated learning. By incorporating global Nesterov momentum into local updates, FedNSAM improves the generalization ability of the global model. The authors theoretically analyze the concept of flatness distance and provide a tighter convergence bound than existing algorithms. Empirical experiments demonstrate the superior performance and efficiency of FedNSAM on various models.

Key Points

  • ▸ Introduction of FedNSAM algorithm for federated learning
  • ▸ Concept of flatness distance and its impact on global model performance
  • ▸ Incorporation of global Nesterov momentum into local updates

Merits

Improved Generalization Ability

FedNSAM enhances the generalization ability of the global model by harmonizing local and global flatness

Tighter Convergence Bound

Theoretical analysis provides a tighter convergence bound than existing algorithms like FedSAM

Demerits

Complexity of Implementation

The incorporation of global Nesterov momentum may add complexity to the implementation of FedNSAM

Expert Commentary

The introduction of FedNSAM marks a significant advancement in federated learning, addressing the long-standing challenge of sharp global minima. By incorporating global Nesterov momentum, FedNSAM provides a novel approach to improve the generalization ability of global models. The theoretical analysis and empirical experiments demonstrate the effectiveness of FedNSAM, making it a promising solution for federated learning applications. However, the complexity of implementation may pose a challenge, and further research is needed to explore the potential of FedNSAM in various scenarios.

Recommendations

  • ✓ Further research on the application of FedNSAM in diverse federated learning scenarios
  • ✓ Investigation into the potential of FedNSAM in addressing other challenges in federated learning, such as communication efficiency and privacy preservation

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