On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression

arXiv:2609.25705 · stat.ML, cs.LG · Submitted 2026-09-22 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-22

Updated: 2026-09-22

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Aggregation — 15× in this paper · explained in 1 episode
• Federated Learning — 11× in this paper · explained in 14 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Federated Learning (FL) — 3× in this paper · explained in 7 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Data Heterogeneity — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes

Sources

• Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
• Adversarially Robust Multitask Adaptive Control
• Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
• Learning subgaussian classes : Upper and minimax bounds

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