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。