Federated Continual Learning

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Federated continual learning studies how clients can learn from evolving tasks or data distributions without sharing raw data or losing previously acquired knowledge. Common approaches include replay buffers and synthetic replay, prototypes and distillation, parameter-importance regularization, and gradient or feature-subspace methods that limit interference; some systems also use client clustering, personalized models, or asynchronous updates. Methods are evaluated on class- and domain-incremental image benchmarks as well as medical, language, multimodal, and mobile settings, where client data and task order may differ. A central tension is balancing adaptation and retention under limited memory, communication, and privacy, while heterogeneous and task-free settings make results difficult to compare and expose weaknesses that average accuracy can hide.

Latest papers

37 papers7 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

One-Shot Federated Model Editing for Device Dynamics at the Edge
Oct 26, 2026Youming Tao, Weijun Yin, Huanyi Xie +5Federated Learning Federated Unlearning
School of Electrical Engineering and Computer Science, TU Berlin, 10623 Berlin, Germany · School of Cyber Science and Technology, Beihang University, Beijing 100191, China · Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100083, China +2
Task Relevance-Driven Prompt Expansion for Federated Continual Learning
Oct 25, 2026Jiahua Dong, Wenqi Liang, Yang Cong +6Continual Learning Federated Continual Learning
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · University of Trento, Trento, Italy · South China University of Technology, Guangzhou, China +4
FedSEAM: Federated Continual Learning of LLMs via Subspace Expert Agreement Merging
Oct 24, 2026Fatema Siddika, Abhijit Chunduru, Zilinghan Li +2Continual Learning for LLMs Federated Continual Learning
Iowa State University, Ames, USA · Argonne National Laboratory, USA · University of Massachusetts Amherst, Amherst, USA
DROD: Dual-Ratio Ordered Dropout for Federated Continual Learning on Heterogeneous Devices
Oct 18, 2026Berk Sezer, Özgür SoysalFederated Continual Learning Heterogeneous Federated Learning
Middle East Technical University · Stanford University
Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs
Oct 14, 2026Shuaijie Ma, Yongkang Hu, Yu Cheng +4Federated Continual Learning Federated Learning
East China Normal University · Shanghai Innovation Institute
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning
Oct 11, 2026Yuriy Dorn, Dmitrii Vishovan, Kirill Novoselov +3Federated Learning Online Federated Learning
Lomonosov Moscow State University · Innopolis University · Moscow Independent Research Institute of Artificial Intelligence +3
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 Trust-Aware Federated 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
Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem
Aug 21, 2026Nazreen Shah, Naveen Kumar Reddy Somireddy, Zubair Shaban +2Federated Continual Learning Catastrophic Forgetting
IIIT Delhi India · IIT Dharwad India
An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
Aug 20, 2026Ioannis Theologitis, Debin Meng, Stylianos Eleftheriadis +2Regularization-Based Continual Learning Federated Continual Learning
Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece · School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting
Aug 15, 2026Zhenyan Liu, Hua Zhang, Haoran Gao +6Federated Learning Heterogeneous Federated Learning
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China · National Engineering Research Center of Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing, China · JIUTIAN Research, China Mobile, Beijing, China +2
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 · Electrical & Computer Engineering, Baylor University, Waco, USA
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
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
Apr 9, 2026Zhuang Qi, Ying-Peng Tang, Lei Meng +4Federated Continual Learning Replay-Based Continual Learning
School of Software, Shandong University, China · College of Computing and Data Science, Nanyang Technological University, Singapore · School of Computer Science and Technology, Harbin Institute of Technology, China
FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation
Mar 30, 2026Tiantian Wang, Xiang Xiang, Simon S. DuFederated Continual Learning Federated Learning
Huazhong University of Science and Technology, Wuhan, China · University of Washington, Seattle, USA
Prototype-Aligned Federated Soft-Prompts for Continual Web Personalization
Mar 29, 2026Canran Xiao, Liwei HouRecommender Systems User Preference Modeling
Shenzhen Campus of Sun Yat-sen University Shenzhen, Guangdong, China · Hunan University Changsha, Hunan, China
ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization
Mar 20, 2026Danish Gufran, Akhil Singampalli, Sudeep PasrichaFederated Learning Federated Continual Learning
Colorado State University, Fort Collins, CO 80523, USA
DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning
Mar 19, 2026Yizhou Han, Di Wu, Blesson VargheseFederated Learning Federated Continual Learning
School of Computer Science, University of St Andrews, UK

Oct 26, 2026J/K move · Enter open · S save

One-Shot Federated Model Editing for Device Dynamics at the Edge

Youming Tao, Weijun Yin, Huanyi Xie, Yanqing Yao+4

School of Electrical Engineering and Computer Science, TU Berlin, 10623 Berlin, Germany · School of Cyber Science and Technology, Beihang University, Beijing 100191, China · Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100083, China+2

Federated Learning Federated Unlearning Federated Continual Learning

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Standard federated learning (FL) requires persistent device connectivity across multiple communication rounds, making it unsuitable for dynamic environments such as vehicular networks, where devices frequently join and leave. One-shot FL offers a promising alternative by completing training in a single communication round. However, one-shot FL suffers from model solidification and cannot readily adapt to subsequent client dynamics, including new client contributions and the withdrawal of previous contributions. We therefore consider exact model editing, which removes withdrawn contributions through unlearning or incorporates newly contributed ones through incremental learning without retraining from scratch. We identify that efficient editing fundamentally hinges on learning stability. To this end, we propose F-SOSA, the first stable one-shot FL algorithm that enables rapid and exact model editing. F-SOSA leverages sub-sampling of local models with carefully tuned rate for stable aggregation and performs curvature-aware model fusion to preserve utility. We prove that F-SOSA achieves vanishing training error and reasonably small generalization error bounds. Building on this stability, we further develop two single-round editing algorithms: F-SOSA-U for unlearning and F-SOSA-I for incremental learning. They ensure that the edited model, along with all intermediate algorithmic states, is statistically indistinguishable from retraining while incurring only an expected

O(ρ)

fraction of its cost, where

ρ

quantifies stability. Experiments demonstrate that our approach matches retraining performance while reducing cumulative time and communication overhead by at least 36% and 22%, respectively, under realistic urban mobility simulations.