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

ArXi:2511.09036v2 Announce Type: replace-cross 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.