Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints

• URL: http://arxiv.org/abs/2604.20031v1
• Date: Tue, 21 Apr 2026 22:22:39 GMT
• Title: Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints
• Abstract summary: We build on the well-known SPO+ approach and develop heterogeneity bounds for the SPO+ surrogate loss.<n>In the case of strongly convex feasible regions, sharper bounds are derived due to the stability.<n>We implement a FedAvg-style DFFL set of experiments on both polyhedral and strongly convex problems.
• Score: 45.88028371034407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider what we refer to as {Decision-Focused Federated Learning (DFFL)} framework, i.e., a predict-then-optimize approach employed by a collection of agents, where each agent's predictive model is an input to a downstream linear optimization problem, and no direct exchange of raw data is allowed. Importantly, clients can differ both in objective functions and in feasibility constraints. We build on the well-known SPO+ approach and develop heterogeneity bounds for the SPO+ surrogate loss in this case. This is accomplished by employing a support function representation of the feasible region, separating (i) objective shift via norm distances between the cost vectors and (ii) feasible-set shift via shape distances between the constraint sets. In the case of strongly convex feasible regions, sharper bounds are derived due to the optimizer stability. Building on these results, we define a heuristic local-versus-federated excess risk decision rule which, under SPO+ risk, gives a condition for when federation can be expected to improve decision quality: the heterogeneity penalty must be smaller than the statistical advantage of pooling data. We implement a FedAvg-style DFFL set of experiments on both polyhedral and strongly convex problems and show that federation is broadly robust in the strongly convex setting, while performance in the polyhedral setting degrades primarily with constraint heterogeneity, especially for clients with many samples. In other words, especially for the strongly convex case, an approach following a direct implementation of FedAvg and SPO+ can still yield promising performance even when the downstream optimization problems are noticeably different.

Related papers

• Preference Data Selection for Mitigating the Alignment Tax in Large Language Models [52.18310687523229]
  BALIGN is a balanced data selection strategy that mitigates catastrophic forgetting while optimizing alignment efficacy.<n>By aggregating three key data-centric features into a unified composite risk score, BALIGN systematically filters out high-risk preference samples.<n>Experiments on standard human preference datasets demonstrate that BALIGN strongly preserves foundational capabilities without compromising alignment gains.
  arXiv  Detail & Related papers  (2026-08-25T07:58:42Z)
• Federated Bilevel Performative Prediction [33.32982501255116]
  We formalize the federated bilevel performatively stable (FBPS) point under a decoupled-risk perspective.<n>We develop two methods to compute the FBPS solution: FBi-RRM, which converges linearly under a contraction condition, and FBi-SGD, a communication-efficient method based on federated hypergradient estimation.
  arXiv  Detail & Related papers  (2026-06-18T02:58:32Z)
• How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings? [58.740078141879984]
  Machine learning models often degrade when they are deployed on a target distribution that differs from the source distributions they were trained on.<n>Recent work in causality-based domain generalization has shown how shared causal structure can induce invariant predictors.<n>In this paper, we study when full or partial causal knowledge can provably improve supervised domain adaptation.
  arXiv  Detail & Related papers  (2026-06-10T21:07:49Z)
• A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints [22.464820932996588]
  This paper proposes a general deep learning framework for dealing with discrete variables.<n>We show that the proposed framework consistently outperforms existing baselines in terms of both system performance and computational efficiency.
  arXiv  Detail & Related papers  (2026-03-17T07:10:56Z)
• Learning Optimal Distributionally Robust Individualized Treatment Rules Integrating Multi-Source Data [3.821271508420626]
  We propose a prior information-based distributionally robust ITR (PDRO-ITR) that maximizes the worst-case policy value over a co-dependent distributional uncertainty set.<n>We establish risk bounds for the PDRO-ITR estimator, which guarantees robust performance under the worst case.
  arXiv  Detail & Related papers  (2026-03-05T14:33:52Z)
• Bulk-Calibrated Credal Ambiguity Sets: Fast, Tractable Decision Making under Out-of-Sample Contamination [8.826173150779145]
  Distributionally robust optimisation (DRO) minimises the worst-case expected loss over an ambiguity set.<n>We show how IP credal sets translate into DRO objectives with interpretable tolerance levels.
  arXiv  Detail & Related papers  (2026-01-29T06:37:36Z)
• Distributionally Robust Federated Learning with Outlier Resilience [8.69285602685459]
  We study distributionally robust federated learning with explicit outlier resilience.<n>We reformulate the problem as a tractable Lagrangian penalty optimization, which admits robustness certificates.<n>Building on this reformulation, we propose the distributionally outlier-robust federated learning algorithm and establish its convergence guarantees.
  arXiv  Detail & Related papers  (2025-09-29T08:42:12Z)
• Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss [16.1456465253627]
  We study multi-source unsupervised domain adaptation, where labeled data are available from multiple source domains and only unlabeled data are observed from the target domain.<n>We propose a novel Group Distributionally Conditional Optimization framework that learns a classifier by minimizing the worst-case cross-entropy loss over the convex combinations of the conditional outcome distributions from sources domains.<n>We establish fast statistical convergence rates for the empirical CG-DRO estimator by constructing two surrogate minimax optimization problems that serve as theoretical bridges.
  arXiv  Detail & Related papers  (2025-07-14T04:21:23Z)
• Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems [53.03951222945921]
  We analyze smoothed (perturbed) policies, adding controlled random perturbations to the direction used by the linear oracle.<n>Our main contribution is a generalization bound that decomposes the excess risk into perturbation bias, statistical estimation error, and optimization error.<n>We illustrate the scope of the results on applications such as vehicle scheduling, highlighting how smoothing enables both tractable training and controlled generalization.
  arXiv  Detail & Related papers  (2024-07-24T12:00:30Z)
• Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning [132.7040981721302]
  We study the Constrained Convex Decision Process (MDP), where the goal is to minimize a convex functional of the visitation measure. Design algorithms for a constrained convex MDP faces several challenges, including handling the large state space.
  arXiv  Detail & Related papers  (2024-02-16T16:35:18Z)
• Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
  We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences. Our method is especially suitable for problems with well-specified likelihoods. We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
  arXiv  Detail & Related papers  (2023-11-08T00:10:21Z)
• Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring [104.19414150171472]
  Attributes skews the current federated learning (FL) frameworks from consistent optimization directions among the clients. We propose disentangled federated learning (DFL) to disentangle the domain-specific and cross-invariant attributes into two complementary branches. Experiments verify that DFL facilitates FL with higher performance, better interpretability, and faster convergence rate, compared with SOTA FL methods.
  arXiv  Detail & Related papers  (2022-06-14T13:12:12Z)
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization [93.78811018928583]
  This paper provides a framework to analyze the convergence of federated heterogeneous optimization algorithms. We propose FedNova, a normalized averaging method that eliminates objective inconsistency while preserving fast error convergence.
  arXiv  Detail & Related papers  (2020-07-15T05:01:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.