Concept Matching: Clustering-based Federated Continual Learning
Xiaopeng Jiang Cristian Borcea
Federated Continual Learning (FCL) has emerged as a promising paradigm that
combines Federated Learning (FL) and Continual Learning (CL). To achieve good
model accuracy, FCL needs to tackle catastrophic forgetting due to concept
drift over time in CL, and to overcome the potential interference among clients
in FL. We propose Concept Matching (CM), a clustering-based framework for FCL
to address these challenges. The CM framework groups the client models into
concept model clusters, and then builds different global models to capture
different concepts in FL over time. In each round, the server sends the global
concept models to the clients. To avoid catastrophic forgetting, each client
selects the concept model best-matching the concept of the current data for
further fine-tuning. To avoid interference among client models with different
concepts, the server clusters the models representing the same concept,
aggregates the model weights in each cluster, and updates the global concept
model with the cluster model of the same concept. Since the server does not
know the concepts captured by the aggregated cluster models, we propose a novel
server concept matching algorithm that effectively updates a global concept
model with a matching cluster model. The CM framework provides flexibility to
use different clustering, aggregation, and concept matching algorithms. The
evaluation demonstrates that CM outperforms state-of-the-art systems and scales
well with the number of clients and the model size.