Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4 papers of 8,653Sort Recent · Most cited
  1. 2026
    FIL-Quant: An Efficient Federated Incremental Compression via Error-Regulated Structured Pruning for Consumer ElectronicsWeiyu Wang, Xiuheng Liao, Jinhua Chen … Keping YuIEEE International Conference on Consumer Electronics (ICCE) · Hosei University · University of Aizu
  2. 2024
    Evaluating Differential Privacy in Federated Continual Learning: A Catastrophic Forgetting-Performance Tradeoff AnalysisChunlu Chen, Zhuotao Lian, Chunhua Su, Kouichi SakuraiTwelfth International Symposium on Computing and Networki… · Kyushu University · University of Aizu
  3. 2024
    A Multi-Head Federated Continual Learning Approach for Improved Flexibility and Robustness in Edge EnvironmentsChunlu Chen, Kevin I‐Kai Wang, Peng Li, Kouichi SakuraiInternational Journal of Networking and Computing · Kyushu University · University of Auckland · +1
  4. 2023
    POSTER: Advancing Federated Edge Computing with Continual Learning for Secure and Efficient PerformanceChunlu Chen, Kevin I‐Kai Wang, Peng Li, Kouichi SakuraiSpringer LNCS · Kyushu University · University of Auckland · +1
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.