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On the effectiveness of partial variance reduction in federated learning with heterogeneous data

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Version 2 2023-12-14, 12:33
Version 1 2023-11-29, 18:24
conference contribution
posted on 2023-12-14, 12:33 authored by Li Bo, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian StichSebastian Stich
Data heterogeneity across clients is a key challenge in federated learning. Prior works address this by either aligning client and server models or using control variates to correct client model drift. Although these methods achieve fast convergence in convex or simple non-convex problems, the performance in over-parameterized models such as deep neural networks is lacking. In this paper, we first revisit the widely used FedAvg algorithm in a deep neural network to understand how data heterogeneity influences the gradient updates across the neural network layers. We observe that while the feature extraction layers are learned efficiently by FedAvg, the substantial diversity of the final classification layers across clients impedes the performance. Motivated by this, we propose to correct model drift by variance reduction only on the final layers. We demonstrate that this significantly outperforms existing benchmarks at a similar or lower communication cost. We furthermore provide proof for the convergence rate of our algorithm.

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Preferred Citation

Li Bo, Mikkel Schmidt, Tommy Alstrøm and Sebastian Stich. On the effectiveness of partial variance reduction in federated learning with heterogeneous data. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2023.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Legacy Posted Date

2023-06-30

Open Access Type

  • Green

BibTeX

@inproceedings{cispa_all_3975, title = "On the effectiveness of partial variance reduction in federated learning with heterogeneous data", author = "Bo, Li and Schmidt, Mikkel N. and Alstrøm, Tommy S. and Stich, Sebastian U.", booktitle="{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}", year="2023", }

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