Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model. There are several federated learning applications where agents may choose to defect permanently$-$essentially withdrawing from the collaboration$-$if they are content with their instantaneous model in that round. This work demonstrates the detrimental impact of such defections on the final model's robustness and ability to generalize. We also show that current federated optimization algorithms fail to disincentivize these harmful defections. We introduce a novel optimization algorithm with theoretical guarantees to prevent defections while ensuring asymptotic convergence to an effective solution for all participating agents. We also provide numerical experiments to corroborate our findings and demonstrate the effectiveness of our algorithm.

翻译：联邦学习是一种机器学习协议，可使大量智能体在多轮协作中生成统一的共识模型。在若干联邦学习应用中，若智能体对当前轮次的即时模型感到满意，可能选择永久性离队——本质上即退出协作。本研究论证了此类离队行为对最终模型的鲁棒性与泛化能力产生的有害影响，同时指出现有联邦优化算法未能有效抑制这种有害离队现象。我们提出了一种具有理论保证的新型优化算法，能够在确保所有参与智能体渐近收敛至有效解的同时防止离队行为。最后通过数值实验佐证研究结论，并验证了所提算法的有效性。