  In this work, we combine the two paradigms: Federated Learning (FL) and
Continual Learning (CL) for text classification task in cloud-edge continuum.
The objective of Federated Continual Learning (FCL) is to improve deep learning
models over life time at each client by (relevant and efficient) knowledge
transfer without sharing data. Here, we address challenges in minimizing
inter-client interference while knowledge sharing due to heterogeneous tasks
across clients in FCL setup. In doing so, we propose a novel framework,
Federated Selective Inter-client Transfer (FedSeIT) which selectively combines
model parameters of foreign clients. To further maximize knowledge transfer, we
assess domain overlap and select informative tasks from the sequence of
historical tasks at each foreign client while preserving privacy. Evaluating
against the baselines, we show improved performance, a gain of (average) 12.4\%
in text classification over a sequence of tasks using five datasets from
diverse domains. To the best of our knowledge, this is the first work that
applies FCL to NLP.
