Class-wise Contribution Estimation via Logit Maximization for Federated Learning

arXiv:2605.18892 · cs.LG, cs.AI, cs.DC · Submitted 2026-05-17 · Read on arXiv

cs.LG, cs.AI, cs.DC

Submitted: 2026-05-17

Updated: 2026-09-11

Comments: Accepted at European Conference on Computer Vision (ECCV) 2026, 23 pages, 7 figures

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

Terminology

• Aggregation — 27× in this paper · explained in 1 episode
• Federated Learning — 24× in this paper · explained in 14 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Regularization — 8× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Pure Label Skew — 5× in this paper
• Calibration — 4× in this paper · explained in 7 episodes
• Federated Learning (FL) — 4× in this paper · explained in 7 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Step Label Skew — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Earth Mover Distance — 2× in this paper
• Interpretability — 2× in this paper · explained in 15 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decentralized Learning — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Local Optimization — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Parsimony — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• Collaborative Fairness in Federated Learning
• Understanding Neural Networks Through Deep Visualization

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