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.

在联邦学习中，我们提出了一种名为 MAP 的算法，旨在解决分布在不完整类别数据上的模型聚合和个性化挑战，通过引入限制性 softmax 替代标准 softmax 以提高模型聚合效果，并利用继承的个性化模型来存储个性化经验。大量实验证明了我们算法的优越性。