Fed-DREAM: Bridging Fragmented Views in Hybrid Federated Learning via Relational Learning

Hybrid federated learning (HBFL) addresses scenarios in which sample and feature spaces are fragmented across clients, so that each client holds only a partial view with limited predictive capacity. Existing HBFL methods aggregate views through feature or prediction sharing with static fusion strategies, missing the inter-client relations essential for a holistic representation. We propose **Fed-DREAM** (Federated Divisive RElational Aggregation and Mixup), a framework for collaborative relational learning in HBFL built on a three-stage pipeline: (1) *federated divisive clustering*, which constructs structurally aligned global clusters without exposing raw data, (2) *multi-sample representation-level mixup* for privacy-preserving synthetic feature sharing, and (3) an*edge-conditioned gated graph neural network* that fuses fragmented views by conditioning message passing on inter-client feature-membership relations. Experiments across tabular, image, and text modalities show that Fed-DREAM consistently outperforms state-of-the-art benchmarks while exhibiting lower membership-inference leakage than the highest-utility baseline.