Decentralized Split Federated Learning with Asynchronous Rotating Committees

Split Federated Learning (SFL) enables collaborative training of machine learning models on resource-constrained IoT devices by combining model splitting with federated optimization. Despite its promise, existing SFL systems fundamentally lack scalability due to centralized server bottlenecks, synchronous training barriers, and reliance on fixed infrastructure, that are major limiting factors in IoT environments. We propose ARC-SFL, a fully asynchronous and decentralized framework for SFL that eliminates permanent servers while preserving accuracy and efficiency benefits. ARC-SFL distributes server-side computation across replicated committee members via our load balancing scheme, and supports asynchronous aggregation to remove straggler blocking. To further reduce communication overhead, ARC-SFL employs event-triggered gossip, enabling peer-to-peer information exchange only when local training has made progress and the learned representation has drifted. Across multiple vision, medical, and audio benchmarks, and on real homogeneous and heterogeneous clusters of IoT devices, we show that ARC-SFL: (i) scales linearly with the number of devices, reaching an order of magnitude higher throughput than synchronous SFL at 100 devices, while remaining robust to device and committee failures; (ii) significantly outperforms decentralized gossip learning on all six tasks by up to 31.2% and, without any centralized server, remains on par with the best centralized task-dependent baselines; and (iii) speeds up the convergence by  3.5x and reduces gossip communication by up to 55% without degrading accuracy on compact models.