Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection. Due to the high communication cost of traditional FL, one-shot federated learning is gaining popularity as a way to reduce communication cost between clients and the server. Most of the existing one-shot FL methods are based on Knowledge Distillation; however, {distillation based approach requires an extra training phase and depends on publicly available data sets or generated pseudo samples.} In this work, we consider a novel and challenging cross-silo setting: performing a single round of parameter aggregation on the local models without server-side training. In this setting, we propose an effective algorithm for Model Aggregation via Exploring Common Harmonized Optima (MA-Echo), which iteratively updates the parameters of all local models to bring them close to a common low-loss area on the loss surface, without harming performance on their own data sets at the same time. Compared to the existing methods, MA-Echo can work well even in extremely non-identical data distribution settings where the support categories of each local model have no overlapped labels with those of the others. We conduct extensive experiments on two popular image classification data sets to compare the proposed method with existing methods and demonstrate the effectiveness of MA-Echo, which clearly outperforms the state-of-the-arts. The source code can be accessed in \url{https://github.com/FudanVI/MAEcho}.

翻译：联邦学习（FL）近年来作为一种保护隐私的新机器学习范式取得了显著进展。由于传统FL通信成本高昂，单次联邦学习作为降低客户端与服务器间通信成本的方式日益受到关注。现有单次FL方法多基于知识蒸馏，然而蒸馏类方法需要额外训练阶段，且依赖公开数据集或生成的伪样本。本文考虑一种新颖且具有挑战性的跨孤岛场景：在无需服务端训练的情况下，对本地模型执行单轮参数聚合。针对该场景，我们提出了一种通过探索共享协调最优进行模型聚合的有效算法（MA-Echo），该算法迭代更新所有本地模型的参数，使其在损失平面上逼近共同低损失区域，同时不损害其自身数据集上的性能。与现有方法相比，MA-Echo即使在极端非相同数据分布（各本地模型支持类别间标签无交集）的场景下仍能有效工作。我们在两个主流图像分类数据集上开展了大量实验，将所提方法与现有方法进行对比，验证了MA-Echo的有效性，其性能显著优于现有最优方法。源代码可通过\url{https://github.com/FudanVI/MAEcho}获取。