Personalised federated learning for Riemannian and Euclidean EEG decoding

arXiv:2609.29037 · stat.ML, cs.LG · Submitted 2026-09-24 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Federated Learning — 9× in this paper · explained in 15 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Federated Learning (FL) — 2× in this paper · explained in 7 episodes
• Stiefel manifold — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
• Federated Learning with Personalization Layers

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
• Online Conformal Prediction for Non-Exchangeable Panel Data
• Deep Time-Series Forecasting in 10 Years: A Survey