FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning

arXiv:2610.09091 · cs.LG, math.OC, stat.ML · Submitted 2026-10-06 · Read on arXiv

cs.LG, math.OC, stat.ML

Submitted: 2026-10-06

Updated: 2026-10-06

Comments: 10 pages main paper + appendix. Accepted at NeurIPS 2026 main track

Code: https://github.com/kziliask/RSPO_Plus

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

Terminology

• Regret — 62× in this paper · explained in 4 episodes
• Personalization — 42× in this paper · explained in 2 episodes
• Regularization — 27× in this paper · explained in 3 episodes
• Federated Learning — 22× in this paper · explained in 17 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Aggregation — 11× in this paper
• Calibration — 11× in this paper · explained in 9 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Core — 6× in this paper · explained in 2 episodes
• Homogeneity — 6× in this paper · explained in 2 episodes
• Alignment — 5× in this paper
• Smart Predict-then-Optimize — 5× in this paper
• Adam — 4× in this paper · explained in 2 episodes
• Computational overhead — 4× in this paper · explained in 2 episodes
• Data Heterogeneity — 4× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 4× in this paper
• Objective Function — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 3 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Institutional Review Board — 2× in this paper
• Modularity — 2× in this paper · explained in 9 episodes
• Bayesian Inference — 1× in this paper · explained in 6 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Federated Learning (FL) — 1× in this paper · explained in 11 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Wavelet Transform — 1× in this paper · explained in 2 episodes

Sources

• Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints
• Federated Learning with Personalization Layers
• Learning with Combinatorial Optimization Layers: a Probabilistic Approach
• Adam: A Method for Stochastic Optimization

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