Federated Continual Learning

Follow this topic Ask about this topic

Momentum

4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 21

All topics

When the Cross-Silo Federation Goes Offline: Continual Learning for Site Onboarding with Limited Unlabeled Data
Oct 4, 2026Ahmadreza Eslaminia, Klara Nahrstedt, Chenhui ShaoClass-Incremental Learning Federated Continual Learning
Coordinated Science Laboratory University of Illinois Urbana-Champaign · Dept. of Mechanical Engineering University of Michigan
ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning
Sep 30, 2026Sungmin Kang, Zhengzhong Tu, Sunwoo LeeFederated Continual Learning Replay-Based Continual Learning
Texas A&M University · Inha University
Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning
Sep 14, 2026Yuxuan Sun, Yuxuan Bai, Tan Chen +2Client Selection in Federated Learning Federated Continual Learning
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China · Department of Electronic Engineering, Tsinghua University, Beijing 100084, China, and the Beijing National Research Center for Information Science and Technology
A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction
Sep 9, 2026Chunxu Zhang, Bo Li, Wenliang Wang +7Generative Replay Federated Continual Learning
PolyU Academy for Artificial Intelligence, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China. · Quantum Life, Hong Kong, China. · Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China. +3
When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks
Jul 30, 2026Zihao Ding, Jun Huang, Liang DongFederated Unlearning Federated Continual Learning
Electrical Engr & Computer Science · South Dakota State University · Brookings, USA +3
FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning
Jul 30, 2026Zekai Chen, Haodong Lu, Shihao Li +5Multimodal Federated Learning Federated Continual Learning
Beijing Institute of Technology, Beijing, China
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
Jul 13, 2026Jing Liu, Chenxuanyin Zou, Jiayang Ren +5Multimodal Federated Learning Generative Replay
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada · College of Future Information Technology, Fudan University, Shanghai 200433, China · Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada +3
Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
Jun 27, 2026Wenhao Yuan, Chenchen Lin, Jian Chen +3Class-Incremental Learning Federated Continual Learning
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China · School of Artificial Intelligence, Sun Yat-sen University, China.
C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift
Jun 16, 2026Davide Domini, Gianluca Aguzzi, Lorenzo Pellegrini +2Federated Learning Aggregation Federated Continual Learning
University of Bologna · Aarhus University
When Generator Replay Degrades: Projected Rehearsal Orchestration for Heterogeneous Federated Class-Incremental Learning
Jun 14, 2026Thinh T. H. Nguyen, Khoa D. Doan, Binh T. Nguyen +2Class-Incremental Learning Federated Continual Learning
VinUniversity, Hanoi, Vietnam · VNU-HCM, University of Science, Ho Chi Minh City, Vietnam · Technische Universität Berlin, Germany
HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning
Jun 13, 2026Thinh T. H. Nguyen, Le-Tuan Nguyen, Minh-Duong Nguyen +4Federated Continual Learning Non-IID Federated Learning
VinUniversity, Hanoi, Vietnam
Accurate and Resource-Efficient Federated Continual Learning
Jun 9, 2026Jebacyril Arockiaraj, Dhruv Parikh, Jayashree Adivarahan +2Communication-Efficient Distributed Training Continual Learning
University of Southern California · Los Angeles, USA · DEVCOM Army Research Office +1
Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data
Jun 9, 2026Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange +1Continual Learning Federated Continual Learning
Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino 1, Pisa, 56122, Italy
FlashbackCL: Mitigating Temporal Forgetting in Federated Learning
Jun 2, 2026Mubarak A. Ojewale, Adriana E. Chis, Jorge M. Cortes-Mendoza +2Distribution Shift Federated Continual Learning
Cloud Competency Centre, National College of Ireland, Dublin, Ireland
Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings
May 29, 2026Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj SharmaFederated Continual Learning Replay-Based Continual Learning
Department of Computer Science, Iowa State University · Department of Computer Science & Engineering, BRAC University · Department of Civil, Construction & Environmental Engineering, Iowa State University
Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems
Apr 22, 2026Beining Wu, Jun HuangMobile Robotics Federated Continual Learning
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, 57006 SD
Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting
Jan 29, 2026Chuan-Chi LaiMulti-Objective Reinforcement Learning Federated RL
Department of Communications Engineering, National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan · Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan
Knowledge-Aware Evolution for Task-Free Streaming Federated Continual Learning with Arbitrary Class Overlap
Jan 27, 2026Sixing Tan, Xianmin LiuContinual Learning Federated Continual Learning
Faculty of Computing Harbin Institute of Technology
Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection
Sep 25, 2025Seohyeon Cha, Huancheng Chen, Haris VikaloFederated Continual Learning Gradient Projection
Department of Electrical and Computer Engineering The University of Texas at Austin
Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
Mar 19, 2025Can Peng, Qianhui Men, Pramit Saha +5Federated Learning Aggregation Federated Continual Learning
University of Oxford, Oxford, United Kingdom · University of Bristol, Bristol, United Kingdom
Federated Class-Incremental Learning with Hierarchical Generative Prototypes
Jun 4, 2024Riccardo Salami, Pietro Buzzega, Matteo Mosconi +2Class-Incremental Learning Prototype-Based Classification
AImageLab - University of Modena and Reggio Emilia, Modena, Italy

Oct 4, 2026, cs.LGJ/K move · Enter open · S save

When the Cross-Silo Federation Goes Offline: Continual Learning for Site Onboarding with Limited Unlabeled Data

Ahmadreza Eslaminia, Klara Nahrstedt, Chenhui Shao

Coordinated Science Laboratory University of Illinois Urbana-Champaign · Dept. of Mechanical Engineering University of Michigan

Class-Incremental Learning Federated Continual Learning Federated Learning

Open paper AskPDF arXiv

An organization often holds too little labeled data to train a model that generalizes, and the records that would supply the rest sit with organizations that cannot release them. Cross-silo federated learning offers a way through, since participants exchange model parameters rather than records, but it ordinarily settles two aspects of the arrangement in advance, the participating sites and the classes the model can predict, and deployment can breach both. A new site joins after training, once the established sites have finished their engagement and gone offline, and its records arrive unlabeled, mixing conditions the model already recognizes with conditions no participant has observed. We present an autonomous three-stage procedure that expands the model entirely at the joining site: reconstruction experts screen for novelty, clustering separates the flagged records into candidate conditions, and class means describe the old classes, all inside one shared representation. Those classes were learned from records that never leave their owners, so the usual defenses against forgetting are unavailable, and the procedure supplies the evidence they would have carried from either of two dissimilar sources, prototypes held by the federation or records held by the joining site. On a real industrial condition-monitoring dataset, run end to end with no label consulted, either source holds old-class accuracy at 0.868 or above with forgetting at most 0.063, and the two differ by 0.021, so a configuration can be chosen by the disclosure it permits rather than the accuracy it delivers. Both keep old- and new-class accuracy in balance where every alternative we measure gives up one for the other, and both retain more of the old classes than distillation- and regularization-based baselines. The balance still holds with only 6 labeled records per arriving condition and 3 retained per old class.

Momentum

Related topics

Latest papers 21