arXiv Machine Learning
FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
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arXiv Machine Learning
Aug 11
SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
arXiv:2602. 22895v2 Announce Type: replace-cross Abstract: Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages.
arXiv AI
Aug 24
SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges
This review discusses how neuroimaging data can be represented as symmetric positive-definite (SPD) matrices and analyzed using the Riemannian geometry of the SPD manifold. It surveys the evolution from modality-specific SPD representations to geometric shallow and deep learning methods, emphasizing how these approaches maintain structural constraints while integrating modern AI techniques. The paper frames SPD matrix learning as a bridge between classical geometric statistics and contemporary machine learning in neuroimaging and brain‑computer interface research.
arXiv Machine Learning
Sep 25
Personalised federated learning for Riemannian and Euclidean EEG decoding
The paper introduces a personalised federated learning approach for EEG decoding, applying it to both a Riemannian SPDNet and a Euclidean EEGNet. In this scheme, all subjects share a common trunk that learns a latent representation, while each subject retains a private head for classification, thereby addressing inter‑subject variability. Experiments on three motor‑imagery datasets show that personalised SPDNet outperforms standard federated learning and centralised training, converges faster, and communicates fewer parameters, and it also surpasses all EEGNet variants on two of the datasets.
Hugging Face Trending Papers
Sep 24
Personalised federated learning for Riemannian and Euclidean EEG decoding
The paper explores personalised federated learning for EEG decoding using two lightweight models: the Riemannian SPDNet and the Euclidean EEGNet. In the personalised approach, all subjects share a common trunk while keeping individual heads, which improves accuracy and reduces communication compared to standard federated learning and centralised training. Experiments on three motor‑imagery datasets show that personalised SPDNet outperforms both standard FL and centralised training, and beats EEGNet on two datasets, though centralised EEGNet remains superior to centralised SPDNet.
arXiv Statistics ML
Oct 27
Projection-Based Federated Learning on Compact Submanifolds with Geometry-Independent Differential Privacy
arXiv:2610.33712v1 Announce Type: new Abstract: Federated learning trains a shared model across clients without exchanging their data, yet the updates they transmit still leak information about indiv...
arXiv AI
Jul 10
FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.