Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation

• URL: http://arxiv.org/abs/2606.22510v1
• Date: Sun, 21 Jun 2026 14:04:33 GMT
• Title: Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation
• Abstract summary: Fed-CausalDiff is a federated causal diffusion framework for do-simulation.<n>The architecture decomposes the evolution of the latent state into a global causal score function and a local confounding score function.<n> Experiments on four datasets demonstrate that Fed-CausalDiff achieves better ATE and policy-value estimation accuracy.
• Score: 3.969626207862676
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observational approach is fundamentally insufficient for interventional inference and policy evaluation, as sequential actions dynamically alter future states. We propose \textbf{Fed-CausalDiff}, a federated causal diffusion framework for do-simulation. The architecture decomposes the evolution of the latent state into a global causal score function and a local confounding score function. This design enables \emph{decoupled synchronisation} (DSS), where clients aggregate only the shared causal mechanism while retaining site-specific confounders locally to handle heterogeneity. Experiments on four datasets demonstrate that Fed-CausalDiff achieves better ATE and policy-value estimation accuracy, offering a favorable trade-off between communication cost and inference fidelity.

Related papers

• FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning [103.45987800174724]
  Federated Learning (FL) confronts a significant challenge known as data heterogeneity, which impairs model performance and convergence.<n>We propose textbfFedGPS, a novel framework that seamlessly integrates statistical distribution and gradient information from others.
  arXiv  Detail & Related papers  (2025-10-23T06:10:11Z)
• CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks [51.43780477302533]
  Contribution-Oriented PFL (CO-PFL) is a novel algorithm that dynamically estimates each client's contribution for global aggregation.<n>CO-PFL consistently surpasses state-of-the-art methods in robustness in personalization accuracy, robustness, scalability and convergence stability.
  arXiv  Detail & Related papers  (2025-10-23T05:10:06Z)
• Scalable Asynchronous Federated Modeling for Spatial Data [5.741482548419374]
  This work proposes an asynchronous federated modeling framework for spatial data based on low-rank Gaussian process approximations.<n>We establish linear convergence with explicit dependence on staleness, a result of standalone theoretical significance.<n> numerical experiments demonstrate that the asynchronous algorithm achieves synchronous performance under balanced resource allocation.
  arXiv  Detail & Related papers  (2025-10-02T08:04:46Z)
• FedSC: Federated Learning with Semantic-Aware Collaboration [12.366529890744822]
  Federated learning (FL) aims to train models collaboratively across clients without sharing data for privacy-preserving.<n>We propose Federated Learning with Semantic-Aware Collaboration (FedSC) to capture client-specific and class-relevant knowledge across heterogeneous clients.
  arXiv  Detail & Related papers  (2025-06-26T05:04:55Z)
• Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation [0.0]
  Causal inference typically assumes centralized access to individual-level data.<n>We address this problem by estimating the Average Treatment Effect (ATE) from decentralized observational data via a Federated Learning (FL) approach.
  arXiv  Detail & Related papers  (2025-05-23T14:32:57Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Federated Causal Discovery from Heterogeneous Data [70.31070224690399]
  We propose a novel FCD method attempting to accommodate arbitrary causal models and heterogeneous data. These approaches involve constructing summary statistics as a proxy of the raw data to protect data privacy. We conduct extensive experiments on synthetic and real datasets to show the efficacy of our method.
  arXiv  Detail & Related papers  (2024-02-20T18:53:53Z)
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation [52.82176415223988]
  We propose federated learning with consensus-oriented generation (FedCOG) FedCOG consists of two key components at the client side: complementary data generation and knowledge-distillation-based model training. Experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-10T18:49:59Z)
• Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach [66.9033666087719]
  This paper extends the inference view and describes a variational inference formulation of federated learning. We apply FedEP on standard federated learning benchmarks and find that it outperforms strong baselines in terms of both convergence speed and accuracy.
  arXiv  Detail & Related papers  (2023-02-08T17:58:11Z)
• FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation [95.85026305874824]
  We introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiency.
  arXiv  Detail & Related papers  (2022-12-14T13:57:01Z)
• AFAFed -- Protocol analysis [3.016628653955123]
  This is a novel A Fair Federated Adaptive learning framework for stream-oriented IoT application environments. We analyze the convergence properties and address the implementation aspects AFAFed.
  arXiv  Detail & Related papers  (2022-06-29T22:12:08Z)
• Robustness and Personalization in Federated Learning: A Unified Approach via Regularization [4.7234844467506605]
  We present a class of methods for robust, personalized federated learning, called Fed+. The principal advantage of Fed+ is to better accommodate the real-world characteristics found in federated training. We demonstrate the benefits of Fed+ through extensive experiments on benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-14T10:04:30Z)

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.