  Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of
things and are becoming an integral of our daily life. When tackling the
evolving learning tasks in real world, such as classifying different types of
objects, DNNs face the challenge to continually retrain themselves according to
the tasks on different edge devices. Federated continual learning is a
promising technique that offers partial solutions but yet to overcome the
following difficulties: the significant accuracy loss due to the limited
on-device processing, the negative knowledge transfer caused by the limited
communication of non-IID data, and the limited scalability on the tasks and
edge devices. In this paper, we propose FedKNOW, an accurate and scalable
federated continual learning framework, via a novel concept of signature task
knowledge. FedKNOW is a client side solution that continuously extracts and
integrates the knowledge of signature tasks which are highly influenced by the
current task. Each client of FedKNOW is composed of a knowledge extractor, a
gradient restorer and, most importantly, a gradient integrator. Upon training
for a new task, the gradient integrator ensures the prevention of catastrophic
forgetting and mitigation of negative knowledge transfer by effectively
combining signature tasks identified from the past local tasks and other
clients' current tasks through the global model. We implement FedKNOW in
PyTorch and extensively evaluate it against state-of-the-art techniques using
popular federated continual learning benchmarks. Extensive evaluation results
on heterogeneous edge devices show that FedKNOW improves model accuracy by
63.24% without increasing model training time, reduces communication cost by
34.28%, and achieves more improvements under difficult scenarios such as large
numbers of tasks or clients, and training different complex networks.
