A Review of Continual Learning in Edge AI

The transition from laboratory-controlled AI training to real-world deployment reveals a critical gap: traditional episodic training paradigms fail to address the dynamic, resource-constrained nature of edge environments. Sustainable continual intelligence represents a transformative paradigm that integrates adaptive learning capabilities with sustainability principles for edge AI systems, enabling perpetual learning and evolution within resource constraints while maintaining operational effectiveness. This paper provides a review of continual learning in edge AI, which examines how federated architectures enable distributed systems to learn sustainably under resource constraints through network-aware…