FedGOTA: Towards Effective Federated Multi-Agent Collaboration via Graph Optimal-Transport Alignment

In multi-agent systems (MAS) powered by large language models (LLMs), communication topologies orchestrate interactions among specialized agents, enabling coordinated reasoning on complex tasks. However, learning these topologies is often coupled with task supervision, making the resulting structures task-specific and difficult to transfer to new tasks. In federated settings, this coupling can further confine clients to task-specific knowledge silos, hindering effective aggregation and cross-client knowledge transfer. To address these limitations, we decouple structure learning from task supervision by introducing a shared structural self-supervised objective. Building on this formulation, we propose FedGOTA, a federated multi-agent collaboration framework via graph optimal-transport alignment. On each client, several complementary collaboration views are fused to train a topology generator with the self-supervised objective, allowing it to produce reusable, question-aware collaboration structures without task labels. At the server, we probe client topology generators with shared semantic anchors to construct graph codebooks, whose optimal-transport alignment guides personalized aggregation to mitigate negative transfer across heterogeneous clients. Extensive experiments on six reasoning benchmarks across four task domains show that FedGOTA achieves competitive performance compared with fourteen baselines spanning single-agent reasoning and multi-agent collaboration.