Federated learning (FL) enables collaborative learning across multiple
clients. In most FL work, all clients train a single learning task. However,
the recent proliferation of FL applications may increasingly require multiple
FL tasks to be trained simultaneously, sharing clients' computing and
communication resources, which we call Multiple-Model Federated Learning
(MMFL). Current MMFL algorithms use naive average-based client-task allocation
schemes that can lead to unfair performance when FL tasks have heterogeneous
difficulty levels, e.g., tasks with larger models may need more rounds and data
to train. Just as naively allocating resources to generic computing jobs with
heterogeneous resource needs can lead to unfair outcomes, naive allocation of
clients to FL tasks can lead to unfairness, with some tasks having excessively
long training times, or lower converged accuracies. Furthermore, in the FL
setting, since clients are typically not paid for their training effort, we
face a further challenge that some clients may not even be willing to train
some tasks, e.g., due to high computational costs, which may exacerbate
unfairness in training outcomes across tasks. We address both challenges by
firstly designing FedFairMMFL, a difficulty-aware algorithm that dynamically
allocates clients to tasks in each training round. We provide guarantees on
airness and FedFairMMFL's convergence rate. We then propose a novel auction
design that incentivizes clients to train multiple tasks, so as to fairly
distribute clients' training efforts across the tasks. We show how our
fairness-based learning and incentive mechanisms impact training convergence
and finally evaluate our algorithm with multiple sets of learning tasks on real
world datasets.

多模型联邦学习算法设计及公平性和激励机制对训练收敛的影响研究。