Venn: Resource Management for Collaborative Learning Jobs

Jiachen Liu, Fan Lai, Eric Ding, Yiwen Zhang, Mosharaf Chowdhury

Conference on Machine Learning and Systems 2025 · Day 4 · Session 11: Federated Learning

This talk introduces **Venn**, a novel resource manager designed to optimize the execution of multiple concurrent collaborative learning jobs across large-scale, heterogeneous edge devices. Presented by Jiachen Liu from the University of Michigan, Venn addresses the increasingly critical challenge of managing shared resources in environments where numerous machine learning tasks, particularly those leveraging private user data, compete for limited and unpredictable device availability. The core problem Venn tackles is the complex resource contention that arises when companies deploy many collaborative learning jobs, such as federated learning, on diverse user devices like smartphones.

AI review

Venn is a competent systems paper on federated learning resource scheduling with a clean core insight — prioritize scarce resources first, then break ties by demand within groups. The 1.87x speedup over random matching is meaningful if you're running multi-job FL at scale. But this is a conference paper presentation dressed up as a talk, and the write-up reads like an LLM-expanded abstract. The technical meat is thin relative to the word count, the resource-aware matching component is handwaved entirely, and there's nothing here an engineer could actually implement without the paper.