Pick-a-back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning

With the explosion of edge intelligence, leveraging federated indirect knowledge has become crucial for boosting individual learners. However, the conventional approach to knowledge reuse often leads to catastrophic forgetting issues. In this paper, we revisit the concept of continual learning in the context of edge intelligence and address the knowledge transfer problem to enhance federated continual learning. Since each learner processes private heterogeneous data, we propose Pick-a-back, a device-to-device knowledge federation framework by selectively reusing the external knowledge with similar behavioral patterns. By borrowing indirect experiences, an edge device can initiate learning from useful knowledge and thus achieve faster yet more generalized knowledge acquisition. Using continual tasks consisting of various datasets on lightweight architectures, we validated that Pick-a-back provides a significant inference improvement of up to 8.0% via selective knowledge federation.