  Since federated learning (FL) has been introduced as a decentralized learning
technique with privacy preservation, statistical heterogeneity of distributed
data stays the main obstacle to achieve robust performance and stable
convergence in FL applications. Model personalization methods have been studied
to overcome this problem. However, existing approaches are mainly under the
prerequisite of fully labeled data, which is unrealistic in practice due to the
requirement of expertise. The primary issue caused by partial-labeled condition
is that, clients with deficient labeled data can suffer from unfair performance
gain because they lack adequate insights of local distribution to customize the
global model. To tackle this problem, 1) we propose a novel personalized
semi-supervised learning paradigm which allows partial-labeled or unlabeled
clients to seek labeling assistance from data-related clients (helper agents),
thus to enhance their perception of local data; 2) based on this paradigm, we
design an uncertainty-based data-relation metric to ensure that selected
helpers can provide trustworthy pseudo labels instead of misleading the local
training; 3) to mitigate the network overload introduced by helper searching,
we further develop a helper selection protocol to achieve efficient
communication with acceptable performance sacrifice. Experiments show that our
proposed method can obtain superior performance and more stable convergence
than other related works with partially labeled data, especially in highly
heterogeneous setting.
