In some real-world applications, data samples are usually distributed on local devices, where federated learning (FL) techniques are proposed to coordinate decentralized clients without directly sharing users' private data. FL commonly follows the parameter server architecture and contains multiple personalization and aggregation procedures. The natural data heterogeneity across clients, i.e., Non-I.I.D. data, challenges both the aggregation and personalization goals in FL. In this paper, we focus on a special kind of Non-I.I.D. scene where clients own incomplete classes, i.e., each client can only access a partial set of the whole class set. The server aims to aggregate a complete classification model that could generalize to all classes, while the clients are inclined to improve the performance of distinguishing their observed classes. For better model aggregation, we point out that the standard softmax will encounter several problems caused by missing classes and propose "restricted softmax" as an alternative. For better model personalization, we point out that the hard-won personalized models are not well exploited and propose "inherited private model" to store the personalization experience. Our proposed algorithm named MAP could simultaneously achieve the aggregation and personalization goals in FL. Abundant experimental studies verify the superiorities of our algorithm.

翻译：在某些实际应用中，数据样本通常分布在本地设备上，联邦学习（FL）技术旨在协调分散的客户端，而无需直接共享用户隐私数据。FL通常遵循参数服务器架构，并包含多个个性化与聚合过程。客户端间的自然数据异构性（即非独立同分布数据）对FL中的聚合和个性化目标构成了挑战。本文聚焦于一种特殊的非独立同分布场景——客户端拥有不完整类别，即每个客户端仅能访问全局类别集合的部分子集。服务器旨在聚合一个能泛化至所有类别的完整分类模型，而客户端则倾向于提升其观测类别的区分性能。为优化模型聚合，我们指出标准softmax会因缺失类别引发若干问题，并提出“受限softmax”作为替代方案。为增强模型个性化，我们指出难以获得的个性化模型尚未得到充分利用，并提出“继承私有模型”以存储个性化经验。我们提出的MAP算法能够同时实现FL中的聚合与个性化目标。大量实验研究验证了该算法的优越性。