Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 6,984Sort Recent · Most cited
  1. 2026
    FedGRAIL: Trustworthy and Efficient Federated Class-Incremental Learning for Long-Tailed IoT Edge IntelligenceXinrong Gong, Zibin Ke, Dan Dai … Kaixiang YangIEEE Internet of Things Journal
  2. 2026
    Gated Adaptation for Continual Learning in Human Activity RecognitionReza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu … Hassan GhasemzadehIEEE Internet of Things Journal
    PDF ↗
  3. 2025
    Few-Shot Class-Incremental Learning With Non-IID Decentralized DataCuiwei Liu, Shijie Xu, Huaijun Qiu … Liang ZhaoIEEE Internet of Things Journal · Shenyang Aerospace University · University of Electro-Communications
    PDF ↗
  4. 2025
    Federated Class-Incremental Learning via Weighted Aggregation and DistillationFeng Wu, Alysa Ziying Tan, Siwei Feng … Yuanlu ChenIEEE Internet of Things Journal · Soochow University · Nanyang Technological University
  5. 2025
    WFSL: Warmup-Based Federated Sequential LearningMohamad Arafeh, Ahmad Hammoud, M. Guizani … Di WuIEEE Internet of Things Journal
  6. 2024
    PI-Fed: Continual Federated Learning With Parameter-Level Importance AggregationLang Yu, Lina Ge, Guanghui Wang … Liang HeIEEE Internet of Things Journal
  7. 2024
    General Federated Class-Incremental Learning With Lightweight Generative ReplayYuanlu Chen, Alysa Ziying Tan, Siwei Feng … Feng WuIEEE Internet of Things Journal · Soochow University · Nanyang Technological University
  8. 2023
    Personalized Federated Continual Learning for Task-Incremental BiometricsDongdong Li, Nan Huang, Zhe Wang, Hai YangIEEE Internet of Things Journal · East China University of Science and Technology
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.