InfiniCL: Elastic Continual Learning for Resource-Constrained Edge Devices
Chenyu Lu, Mengyang Liu, Fang Dong, Borui Li, Ruiting Zhou, Shiyao Ji · 2025
On-device continual learning (CL) enables lifelong and privacy-preserving learning for various edge intelligent applications. Increasing the number of model parameters as new learning tasks emerge is effective in ensuring learning quality but inefficient in memory cost, especially for resource-constrained devices. In this paper, we introduce InfiniCL, the first ondevice CL system that dynamically balances memory cost and learning quality. A key idea behind InfiniCL is elastic continual learning: selectively freezing layers in the expanding model and periodically distilling the model, preventing unbounded memory growth while preserving learning quality for new tasks. This novel CL paradigm opens a new challenging problem: how to decide the memory allocation of the model and data to achieve better learning Quality of Service (QoS) under the limited memory budget? To alleviate this challenge, we further propose a Bayesian Optimization-driven algorithm to jointly optimize layer freezing selection and data-model memory allocation. Evaluations show that InfiniCL outperforms state-of-the-art methods on diverse memory constraints, achieving 5.34-7.15% and 2.72-9.36% higher accuracy on CIFAR-100 and ImageNet-100, respectively.