DAWN: Distributed LLM Multi-Agent Workflow Synthesis

Large language models (LLMs) have recently empowered multi-agent systems (MAS) to achieve remarkable advances in collaborative reasoning and complex task automation. The effectiveness of these systems fundamentally depends on the design of adaptive communication graphs—the underlying workflows that coordinate agent interactions. However, in real-world scenarios, strict privacy constraints often silo data across organizations, and client distributions are highly non-IID, posing major challenges for synthesizing such workflows. In this work, we are the first to systematically study distributed multi-agent workflow synthesis under these privacy and heterogeneity constraints, and we introduce the Difficulty-Based Skew (DBS) benchmark to emulate such challenging environments. Drawing inspiration from federated graph learning (FGL)—which has primarily focused on classification over static graphs—we identify a critical gap: existing FGL methods do not address the generative design of communication topologies. We reveal two fundamental obstacles to generative workflow synthesis in this setting: (i) workflow specialization conflict, where agents optimized for different task distributions generate incompatible communication patterns that resist meaningful aggregation, and (ii) structural communication shift, where locally optimal agent interaction graphs fail to compose into globally coherent multi-agent workflows. To address these challenges, we propose DAWN, a federated framework that integrates two key innovations: Parametric Resonance, which robustly aggregates heterogeneous local updates via layer-wise SVD-based denoising and alignment, and Structural Gravity, which regularizes local workflow generation by penalizing the Fusion Gromov-Wasserstein distance to a set of prototype communication graphs, ensuring global structural coherence without stifling local adaptation. Experiments on the DBS benchmark show that DAWN surpasses baselines in global task success and reduces inter-client graph divergence, laying a solid foundation for privacy-preserving, adaptive MAS workflow design in heterogeneous settings.