Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers from significant performance degradation when client data distributions are heterogeneous. Many previous FL algorithms have addressed this issue by introducing various proximal restrictions. These restrictions aim to encourage global alignment by constraining the deviation of local learning from the global objective. However, they inherently limit local learning by interfering with the original local objectives. Recently, an alternative approach has emerged to improve local learning generality. By obtaining local models within a smooth loss landscape, this approach mitigates conflicts among different local objectives of the clients. Yet, it does not ensure stable global alignment, as local learning does not take the global objective into account. In this study, we propose Federated Stability on Learning (FedSoL), which combines both the concepts of global alignment and local generality. In FedSoL, the local learning seeks a parameter region robust against proximal perturbations. This strategy introduces an implicit proximal restriction effect in local learning while maintaining the original local objective for parameter update. Our experiments show that FedSoL consistently achieves state-of-the-art performance on various setups.

翻译：联邦学习通过聚合各客户端的本地训练模型来构建全局模型。虽然联邦学习能在保障数据隐私的前提下训练模型，但当客户端数据分布呈现异构性时，其性能往往会显著下降。诸多先前联邦学习算法通过引入各类近端约束来应对该问题。这些约束旨在限制本地学习偏离全局目标的程度，从而促进全局对齐。然而，它们本质上干扰了原始局部目标，限制了本地学习。近期出现了另一种提升本地学习普适性的方法：通过在平滑损失景观中获取局部模型，该方法可缓解各客户端局部目标间的冲突。但由于本地学习未考虑全局目标，该方法无法确保稳定的全局对齐。本研究中，我们提出"学习稳定性联邦优化"（FedSoL），该算法融合了全局对齐与局部普适性两种理念。在FedSoL中，本地学习致力于寻找对近端扰动具有鲁棒性的参数区域。该策略在参数更新时既能保留原始局部目标，又能在本地学习中引入隐式近端约束效应。实验表明，FedSoL在各种设定下均持续取得最优性能。