  Most existing federated learning methods assume that clients have fully
labeled data to train on, while in reality, it is hard for the clients to get
task-specific labels due to users' privacy concerns, high labeling costs, or
lack of expertise. This work considers the server with a small labeled dataset
and intends to use unlabeled data in multiple clients for semi-supervised
learning. We propose a new framework with a generalized model, Federated
Incremental Learning (FedIL), to address the problem of how to utilize labeled
data in the server and unlabeled data in clients separately in the scenario of
Federated Learning (FL). FedIL uses the Iterative Similarity Fusion to enforce
the server-client consistency on the predictions of unlabeled data and uses
incremental confidence to establish a credible pseudo-label set in each client.
We show that FedIL will accelerate model convergence by Cosine Similarity with
normalization, proved by Banach Fixed Point Theorem. The code is available at
https://anonymous.4open.science/r/fedil.
