Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate such behavior through the lens of geometry of the loss and Hessian eigenspectrum, linking the model's lack of generalization capacity to the sharpness of the solution. Motivated by prior studies connecting the sharpness of the loss surface and the generalization gap, we show that i) training clients locally with Sharpness-Aware Minimization (SAM) or its adaptive version (ASAM) and ii) averaging stochastic weights (SWA) on the server-side can substantially improve generalization in Federated Learning and help bridging the gap with centralized models. By seeking parameters in neighborhoods having uniform low loss, the model converges towards flatter minima and its generalization significantly improves in both homogeneous and heterogeneous scenarios. Empirical results demonstrate the effectiveness of those optimizers across a variety of benchmark vision datasets (e.g. CIFAR10/100, Landmarks-User-160k, IDDA) and tasks (large scale classification, semantic segmentation, domain generalization).

通过研究损失和Hessian特征谱的几何性质，我们研究了联邦设置中模型的不良性能和泛化失败，发现使用SAM或ASAM本地训练客户端并在服务器端平均随机权重（SWA）可以显着提高FL中的泛化能力并有助于缩小与中央模型的差距，在各种基准视觉数据集和任务上都表现出良好的效果。

通过寻求平坦的极小值改善联邦学习的泛化能力