Very few methods for hybrid federated learning, where clients only hold subsets of both features and samples, exist. Yet, this scenario is extremely important in practical settings. We provide a fast, robust algorithm for hybrid federated learning that hinges on Fenchel Duality. We prove the convergence of the algorithm to the same solution as if the model is trained centrally in a variety of practical regimes. Furthermore, we provide experimental results that demonstrate the performance improvements of the algorithm over a commonly used method in federated learning, FedAvg, and an existing hybrid FL algorithm, HyFEM. We also provide privacy considerations and necessary steps to protect client data.

翻译：针对混合联邦学习中客户端仅持有部分特征和样本子集这一实际场景，目前鲜有有效方法。然而该场景在实际应用中至关重要。我们提出一种基于Fenchel对偶的快速鲁棒混合联邦学习算法。我们证明了该算法在多种实际场景下收敛于与集中式训练相同的解。此外，实验结果展示了该算法相较于联邦学习常用方法FedAvg及现有混合联邦学习算法HyFEM的性能提升。我们还提供了隐私保护相关考量及保护客户端数据的必要措施。