Federated and continual learning for classification tasks in a society of devices
CiTIUS (Centro Singular de Investigación en Tecnoloxías Intelixentes)（CiTIUS（智能技术卓越研究中心））；Universidade de Santiago de Compostela（圣地亚哥德孔波斯特拉大学）；CITIC（CITIC（信息与通信技术研究中心））；Universidade da Coruña（拉科鲁尼亚大学）
Fernando E. Casado, Dylan Lema, Roberto Iglesias, Carlos V. Regueiro, Senén Barro
Today we live in a context in which devices are increasingly interconnected and sensorized and are almost ubiquitous. Deep learning has become in recent years a popular way to extract knowledge from the huge amount of data that these devices are able to collect. Nevertheless, centralized state-of-the-art learning methods have a number of drawbacks when facing real distributed problems, in which the available information is usually private, partial, biased and evolving over time. Federated learning is a popular framework that allows multiple distributed devices to train models remotely, collaboratively, and preserving data privacy. However, the current proposals in federated learning focus on deep architectures that in many cases are not feasible to implement in non-dedicated devices such as smartphones. Also, little research has been done regarding the scenario where data distribution changes over time in unforeseen ways, causing what is known as concept drift. Therefore, in this work we want to present Light Federated and Continual Consensus (LFedCon2), a new federated and continual architecture that uses light, traditional learners. Our method allows powerless devices (such as smartphones or robots) to learn in real time, locally, continuously, autonomously and from users, but also improving models globally, in the cloud, combining what is learned locally, in the devices. In order to test our proposal, we have applied it in a heterogeneous community of smartphone users to solve the problem of walking recognition. The results show the advantages that LFedCon2 provides with respect to other state-of-the-art methods.
Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings
Department of Computer Science, Iowa State University（爱荷华州立大学计算机科学系）；Department of Computer Science & Engineering, BRAC University（BRAC大学计算机科学与工程系）；Department of Civil, Construction & Environmental Engineering, Iowa State University（爱荷华州立大学土木、建设与环境工程系）
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）
FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Software Engineering, TongJi University（同济大学软件工程学院）
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）
Federated Continual Learning: Concepts, Challenges, and Solutions
Faculty of Computer Science, University of New Brunswick（新 Brunswick大学计算机科学学院）；Department of Electrical and Computer Engineering, University of Windsor（温莎大学电气与计算机工程系）
Resource-Constrained Federated Continual Learning: What Does Matter?
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）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）
Accurate Forgetting for Heterogeneous Federated Continual Learning
Institute for Artificial Intelligence, Tsinghua University（清华大学人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；The University of Auckland（奥克兰大学）；RIKEN（理化学研究所）；Hong Kong Baptist University（香港浸会大学）；DataCanvas Technology Co., Ltd.（数帆科技有限公司）；The University of Tokyo（东京大学）
Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
Tsinghua University（清华大学）；Peking University（北京大学）；University of Bristol（布里斯托大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The University of Melbourne（墨尔本大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）
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）
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning
1 Center for Computational \& Data Sciences, Independent University, Bangladesh；2 Curtin University；Center for Computational \& Data Sciences Independent University, Bangladesh；School of Electrical Engineering, Computing \& Mathematical Sciences (EECMS) Curtin University
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）
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning
National University of Singapore（新加坡国立大学）；Southeast University（东南大学）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；Case Western Reserve University（凯斯西储大学）
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）
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning
China University of Petroleum（中国石油大学）；WeBank（微众银行）；Pazhou Laboratory（琶洲实验室）；South China University of Technology（华南理工大学）；The Hong Kong University of Science and Technology（香港科技大学）
An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
Information Technologies Institute, Centre for Research and Technology Hellas（希腊研究与技术中心信息技术研究所）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽女王学院电子工程与计算机科学学院）
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）
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
The University of British Columbia（英属哥伦比亚大学）；Fudan University（复旦大学）；Royal College of Science, Imperial College London（伦敦帝国理工学院皇家科学学院）；Dyson School of Design Engineering（戴森设计工程学院）；Suzhou Institute of Biomedical Engineering and Technology (SIBET), Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；East China Normal University（华东师范大学）
Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Control
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology（电子与电气工程学院，河内科学技术大学）；Department of Intelligent Computing and Data Science, VinUniversity（智能计算与数据科学系，文大学）；Department of Electrical and Computer Engineering, University of Saskatchewan（电气与计算机工程系，萨斯喀彻温大学）；School of Electrical and Data Engineering, University of Technology Sydney（电气与数据工程学院，悉尼技术大学）；School of Electricals and Electronics, Hanoi University of Science and Technology（电子与电气工程学院，河内科学技术大学）；School of Computer Science and Statistics, Trinity College Dublin（计算机科学与统计学学院，都柏林信任学院）
C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning
Wangxuan Institute of Computer Technology Peking University（北京大学王轩计算机技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Inner Mongolia University of Technology（内蒙古科技大学）
