Abstract: In recent years, collaborative learning (CL) has emerged as a promising approach for machine learning (ML) and data science across distributed edge devices. As the deployment of CL jobs increases, they inevitably contend for limited resources.However, efficient resource scheduling in this context is challenging because of the *ephemeral nature and resource heterogeneity of devices*, coupled with the *overlapping resource requirements of diverse CL jobs*.Existing resource managers often assign devices to CL jobs randomly for simplicity and scalability, but this approach compromises job efficiency.In this paper, we present Auxo, a CL resource manager that efficiently schedules ephemeral, heterogeneous devices among multiple CL jobs to reduce the average job completion time (JCT). Auxo formulates the *Intersection Resource Scheduling (IRS)* problem to identify complex resource contention among multiple CL jobs. It then proposes a contention-aware scheduling heuristic to minimize the average scheduling delay. Furthermore, it proposes a resource-aware device-to-job matching heuristic to optimize response collection time by mitigating stragglers. Our evaluation shows that, compared to the state-of-the-art CL resource managers, Auxo improves the average JCT by up to $1.88\times$. The code is available at https://github.com/SymbioticLab/Venn.