ETH AI Center（ETH AI中心）；ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；Institute of Neuroinformatics - University of Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）
Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe, Thomas Hofmann
备注：
(10 pages, Will appear in the proceedings of CoLLAs 2024)
Continual learning is the problem of integrating new information in a model while retaining the knowledge acquired in the past. Despite the tangible improvements achieved in recent years, the problem of continual learning is still an open one. A better understanding of the mechanisms behind the successes and failures of existing continual learning algorithms can unlock the development of new successful strategies. In this work, we view continual learning from the perspective of the multi-task loss approximation, and we compare two alternative strategies, namely local and global approximations. We classify existing continual learning algorithms based on the approximation used, and we assess the practical effects of this distinction in common continual learning settings.Additionally, we study optimal continual learning objectives in the case of local polynomial approximations and we provide examples of existing algorithms implementing the optimal objectives
Global Convergence of Continual Learning on Non-IID Data
Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, CAS（中国科学院香港创新研究院人工智能与机器人中心）；Academy of Mathematics and Systems Science, CAS（中国科学院数学与系统科学研究院）；Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Joint Continual Learning of Local Language Models and Cloud Offloading Decisions with Budget Constraints
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN（电子与计算机工程学院，普渡大学）；Department of Computer Science, University of Exeter, UK（计算机科学系，埃克塞特大学）
Personalized Federated Continual Learning via Multi-granularity Prompt
School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）；Webank（微众银行）；College of Computer Science, Sichuan University（四川大学计算机学院）；JD Intelligent Cities Research（京东城市智能研究院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
AFCL: Analytic Federated Continual Learning for Spatio-Temporal Invariance of Non-IID Data
Peking University, China（北京大学）；South China University of Technology, China（华南理工大学）；Central South University, China（中南大学）；Beijing Normal University, China（北京师范大学）；University of Southampton, UK（南安普顿大学）；University of Maryland, Baltimore County, USA（马里兰大学巴尔的摩县分校）
Prototype-Guided Non-Exemplar Continual Learning for Cross-subject EEG Decoding
Dept. of Artificial Intelligence Korea University Seoul, Republic of Korea（人工智能系 韩国大学首尔共和国）；Cognitive Engineering Korea University Seoul, Republic of Korea（认知工程 韩国大学首尔共和国）
Federated Continual Learning via Knowledge Fusion: A Survey
Nanyang Technological University（南洋理工大学）；JD iCity, JD Technology（京东科技京东智城）；JD Intelligent Cities Research & Institute of Artificial Intelligence, Southwest Jiaotong University（西南交通大学京东智慧城市研究院与人工智能研究所）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
Convergence of Continual Learning in Homogeneous Deep Networks
Blavatnik School of Computer Science and AI, Tel Aviv University（塔夫茨大学Blavatnik计算机科学与人工智能学院）；Princeton University（普林斯顿大学）；Department of Electrical and Computing Engineering, Technion（技术学院电子与计算工程系）
Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation
School of Computer Science and Engineering（计算机科学与工程学院）；Vellore Institute of Technology（韦洛尔理工学院）
Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning
University of Western Ontario（西安大略大学）；Vector Institute（矢量研究院）；Beihang University（北京航空航天大学）；Mcgill University（麦吉尔大学）；Laval University（拉瓦尔大学）；Mila（米拉研究所）
Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Deakin University（德肯大学）；Harbin Engineering University（哈尔滨工程大学）
Variational Bayes for Federated Continual Learning
National Engineering Research Center for Big Data Technology and System（国家大数据技术与系统工程研究中心）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Lehigh University（里海大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Tencent AI Lab（腾讯AI Lab）
Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
Southwestern University of Finance and Economics（西南财经大学）；University of Electronic Science and Technology of China（电子科技大学）；WeBank（微众银行）；Southwest Jiaotong University（西南交通大学）；Hong Kong University of Science and Technology（香港科技大学）
