Research map / Federated learning and privacy / Personalized and heterogeneous FL

FedScar: Correcting Geometric Bias for Flatness-Consistent Federated Learning

ICML 2026 · Federated learning and privacy › Personalized and heterogeneous FL · approach: Non-IID federated learning

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Closest papers by meaning

• FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning (NeurIPS 2026 · similarity 0.92)
• Rethinking the Flat Minima Searching in Federated Learning (ICML 2024 · similarity 0.90)
• Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization (ICML 2024 · similarity 0.90)
• FedWMSAM: Fast and Flat Federated Learning via Weighted Momentum and Sharpness-Aware Minimization (NeurIPS 2025 · similarity 0.90)
• Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning (CVPR 2025 · similarity 0.89)
• Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning (CVPR 2025 · similarity 0.89)