FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913059100950528 |
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| author | Pautrel, Thibault Bouchard, Florent Mian, Ammar Ginolhac, Guillaume |
| author_facet | Pautrel, Thibault Bouchard, Florent Mian, Ammar Ginolhac, Guillaume |
| contents | We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-constrained parameters. Unlike standard Euclidean averaging, which violates orthogonality, our approach preserves geometric structure through two efficient aggregation strategies: ProjAvg, projecting arithmetic means onto the Stiefel manifold, and RLAvg, approximating tangent-space averaging via retractions and liftings. Both methods are computationally efficient, independent of the optimizer, and enable scalable federated learning for signal processing applications whose features are SPD matrices. Simulations on EEG motor imagery benchmarks show that FedSPDnet outperforms federated EEGnet in F1 score and robustness to federation and partial participation, while using fewer parameters per communication round. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22494 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet Pautrel, Thibault Bouchard, Florent Mian, Ammar Ginolhac, Guillaume Machine Learning We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-constrained parameters. Unlike standard Euclidean averaging, which violates orthogonality, our approach preserves geometric structure through two efficient aggregation strategies: ProjAvg, projecting arithmetic means onto the Stiefel manifold, and RLAvg, approximating tangent-space averaging via retractions and liftings. Both methods are computationally efficient, independent of the optimizer, and enable scalable federated learning for signal processing applications whose features are SPD matrices. Simulations on EEG motor imagery benchmarks show that FedSPDnet outperforms federated EEGnet in F1 score and robustness to federation and partial participation, while using fewer parameters per communication round. |
| title | FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.22494 |