FEDERATED SEMI-SUPERVISED LEARNING WITH INTER-CLIENT CONSISTENCY & DISJOINT LEARNING

Abstract

While existing federated learning approaches mostly require that clients have fullylabeled data to train on, in realistic settings, data obtained at the client-side often comes without any accompanying labels. Such deficiency of labels may result from either high labeling cost, or difficulty of annotation due to the requirement of expert knowledge. Thus the private data at each client may be either partly labeled, or completely unlabeled with labeled data being available only at the server, which leads us to a new practical federated learning problem, namely Federated Semi-Supervised Learning (FSSL). In this work, we study two essential scenarios of FSSL based on the location of the labeled data. The first scenario considers a conventional case where clients have both labeled and unlabeled data (labels-at-client), and the second scenario considers a more challenging case, where the labeled data is only available at the server (labels-at-server). We then propose a novel method to tackle the problems, which we refer to as Federated Matching (FedMatch). FedMatch improves upon naive combinations of federated learning and semi-supervised learning approaches with a new inter-client consistency loss and decomposition of the parameters for disjoint learning on labeled and unlabeled data. Through extensive experimental validation of our method in the two different scenarios, we show that our method outperforms both local semi-supervised learning and baselines which naively combine federated learning with semi-supervised learning.

1. INTRODUCTION

Federated Learning (FL) (McMahan et al., 2017; Zhao et al., 2018; Li et al., 2018; Chen et al., 2019a; b) , in which multiple clients collaboratively learn a global model via coordinated communication, has been an active topic of research over the past few years. The most distinctive difference of federated learning from distributed learning is that the data is only privately accessible at each local client, without inter-client data sharing. Such decentralized learning brings us numerous advantages in addressing real-world issues such as data privacy, security, and access rights. For example, for on-device learning of mobile devices, the service provider may not directly access local data since they may contain privacy-sensitive information. In healthcare domains, the hospitals may want to improve their clinical diagnosis systems without sharing the patient records. Existing federated learning approaches (McMahan et al., 2017; Wang et al., 2020; Li et al., 2018) handle these problems by aggregating the locally learned model parameters. A common limitation is that they only consider supervised learning settings, where the local private data is fully labeled. Yet, the assumption that all of the data examples may include sophisticate annotations is not realistic for real-world applications. Suppose that we perform on-device federated learning, the users may not want to spend their time and efforts in annotating the data, and the participation rate across the users may largely differ. Even in the case of enthusiastic users may not be able to fully label all the data in the device, which will leave the majority of the data as unlabeled (See Figure 1 (a) ). Moreover, in some scenarios, the users may not have sufficient expertise to correctly label the data. For instance, suppose that we have a workout app that automatically evaluates and corrects one's body posture. In this case, the end users may not be able to evaluate his/her own body posture at all (See Figure 1 

