  Traditional federated classification methods, even those designed for non-IID
clients, assume that each client annotates its local data with respect to the
same universal class set. In this paper, we focus on a more general yet
practical setting, non-identical client class sets, where clients focus on
their own (different or even non-overlapping) class sets and seek a global
model that works for the union of these classes. If one views classification as
finding the best match between representations produced by data/label encoder,
such heterogeneity in client class sets poses a new significant challenge --
local encoders at different clients may operate in different and even
independent latent spaces, making it hard to aggregate at the server. We
propose a novel framework, FedAlign, to align the latent spaces across clients
from both label and data perspectives. From a label perspective, we leverage
the expressive natural language class names as a common ground for label
encoders to anchor class representations and guide the data encoder learning
across clients. From a data perspective, during local training, we regard the
global class representations as anchors and leverage the data points that are
close/far enough to the anchors of locally-unaware classes to align the data
encoders across clients. Our theoretical analysis of the generalization
performance and extensive experiments on four real-world datasets of different
tasks confirm that FedAlign outperforms various state-of-the-art (non-IID)
federated classification methods.
