Online model selection involves selecting a model from a set of candidate models 'on the fly' to perform prediction on a stream of data. The choice of candidate models henceforth has a crucial impact on the performance. Although employing a larger set of candidate models naturally leads to more flexibility in model selection, this may be infeasible in cases where prediction tasks are performed on edge devices with limited memory. Faced with this challenge, the present paper proposes an online federated model selection framework where a group of learners (clients) interacts with a server with sufficient memory such that the server stores all candidate models. However, each client only chooses to store a subset of models that can be fit into its memory and performs its own prediction task using one of the stored models. Furthermore, employing the proposed algorithm, clients and the server collaborate to fine-tune models to adapt them to a non-stationary environment. Theoretical analysis proves that the proposed algorithm enjoys sub-linear regret with respect to the best model in hindsight. Experiments on real datasets demonstrate the effectiveness of the proposed algorithm.

翻译：在线模型选择涉及从候选模型集合中“实时”选择模型，以对数据流进行预测。候选模型的选择对性能具有关键影响。虽然采用更大的候选模型集合自然能带来更强的模型选择灵活性，但在内存受限的边缘设备上执行预测任务时，这可能是不可行的。面对这一挑战，本文提出了一种在线联邦模型选择框架，其中一组学习者（客户端）与具有充足内存的服务器交互，使得服务器存储所有候选模型。然而，每个客户端仅选择存储适合其内存的子集模型，并使用其中一个存储的模型执行自身的预测任务。此外，通过采用所提出的算法，客户端与服务器协作微调模型，使其适应非平稳环境。理论分析证明，该算法相对于事后最优模型具有次线性遗憾。在真实数据集上的实验验证了所提出算法的有效性。