  In a privacy-focused era, Federated Learning (FL) has emerged as a promising
machine learning technique. However, most existing FL studies assume that the
data distribution remains nearly fixed over time, while real-world scenarios
often involve dynamic and continual changes. To equip FL systems with continual
model evolution capabilities, we focus on an important problem called Federated
Continual Novel Class Learning (FedCN) in this work. The biggest challenge in
FedCN is to merge and align novel classes that are discovered and learned by
different clients without compromising privacy. To address this, we propose a
Global Alignment Learning (GAL) framework that can accurately estimate the
global novel class number and provide effective guidance for local training
from a global perspective, all while maintaining privacy protection.
Specifically, GAL first locates high-density regions in the representation
space through a bi-level clustering mechanism to estimate the novel class
number, with which the global prototypes corresponding to novel classes can be
constructed. Then, GAL uses a novel semantic weighted loss to capture all
possible correlations between these prototypes and the training data for
mitigating the impact of pseudo-label noise and data heterogeneity. Extensive
experiments on various datasets demonstrate GAL's superior performance over
state-of-the-art novel class discovery methods. In particular, GAL achieves
significant improvements in novel-class performance, increasing the accuracy by
5.1% to 10.6% in the case of one novel class learning stage and by 7.8% to
17.9% in the case of two novel class learning stages, without sacrificing
known-class performance. Moreover, GAL is shown to be effective in equipping a
variety of different mainstream FL algorithms with novel class discovery and
learning capability, highlighting its potential for many real-world
applications.
