  Federated learning (FL) is an emerging distributed machine learning method
that empowers in-situ model training on decentralized edge devices. However,
multiple simultaneous training activities could overload resource-constrained
devices. In this work, we propose a smart multi-tenant FL system, MuFL, to
effectively coordinate and execute simultaneous training activities. We first
formalize the problem of multi-tenant FL, define multi-tenant FL scenarios, and
introduce a vanilla multi-tenant FL system that trains activities sequentially
to form baselines. Then, we propose two approaches to optimize multi-tenant FL:
1) activity consolidation merges training activities into one activity with a
multi-task architecture; 2) after training it for rounds, activity splitting
divides it into groups by employing affinities among activities such that
activities within a group have better synergy. Extensive experiments
demonstrate that MuFL outperforms other methods while consuming 40% less
energy. We hope this work will inspire the community to further study and
optimize multi-tenant FL.
