  Given the ability to model more realistic and dynamic problems, Federated
Continual Learning (FCL) has been increasingly investigated recently. A
well-known problem encountered in this setting is the so-called catastrophic
forgetting, for which the learning model is inclined to focus on more recent
tasks while forgetting the previously learned knowledge. The majority of the
current approaches in FCL propose generative-based solutions to solve said
problem. However, this setting requires multiple training epochs over the data,
implying an offline setting where datasets are stored locally and remain
unchanged over time. Furthermore, the proposed solutions are tailored for
vision tasks solely. To overcome these limitations, we propose a new
modality-agnostic approach to deal with the online scenario where new data
arrive in streams of mini-batches that can only be processed once. To solve
catastrophic forgetting, we propose an uncertainty-aware memory-based approach.
In particular, we suggest using an estimator based on the Bregman Information
(BI) to compute the model's variance at the sample level. Through measures of
predictive uncertainty, we retrieve samples with specific characteristics, and
- by retraining the model on such samples - we demonstrate the potential of
this approach to reduce the forgetting effect in realistic settings.
