FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD

Zhenyuan Huang; Hui Zhang; Wenzhong Tang; Haijun Yang

AAAI 2026technical0 citations

FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD

Zhenyuan Huang, Hui Zhang, Wenzhong Tang, Haijun Yang

Abstract

Amid growing demands for data privacy and advances in computational infrastructure, federated learning (FL) has emerged as a prominent distributed learning paradigm. Nevertheless, differences in data distribution (such as covariate and semantic shifts) severely affect its reliability in real-world deployments. To address this issue, we propose FedSDWC, a causal inference method that integrates both invariant and variant features. FedSDWC infers causal semantic representations by modeling the weak causal influence between invariant and variant features, effectively overcoming the limitations of existing invariant learning methods in accurately capturing invariant features and directly constructing causal representations. This approach significantly enhances FL

BibTeX

@inproceedings{aaai2026_fedsdwcfederated,
  title = {FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD},
  author = {Zhenyuan Huang and Hui Zhang and Wenzhong Tang and Haijun Yang},
  booktitle = {AAAI 2026},
  year = {2026}
}