  Federated learning (FL) is an emerging distributed machine learning method
that empowers in-situ model training on decentralized edge devices. However,
multiple simultaneous FL tasks could overload resource-constrained devices. In
this work, we propose the first FL system to effectively coordinate and train
multiple simultaneous FL tasks. We first formalize the problem of training
simultaneous FL tasks. Then, we present our new approach, MAS (Merge and
Split), to optimize the performance of training multiple simultaneous FL tasks.
MAS starts by merging FL tasks into an all-in-one FL task with a multi-task
architecture. After training for a few rounds, MAS splits the all-in-one FL
task into two or more FL tasks by using the affinities among tasks measured
during the all-in-one training. It then continues training each split of FL
tasks based on model parameters from the all-in-one training. Extensive
experiments demonstrate that MAS outperforms other methods while reducing
training time by 2x and reducing energy consumption by 40%. We hope this work
will inspire the community to further study and optimize training simultaneous
FL tasks.
