Conference Paper2025Cited by 2

FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning

Rakibul Hasan Rajib, Md Akil Raihan Iftee, Mir Sazzat Hossain, A. K. M. Mahbubur Rahman, Sajib Mistry, M Ashraful Amin, Amin Ahsan Ali

2025 International Joint Conference on Neural Networks (IJCNN)

IEEE, pp. 1–8

DOI: 10.1109/ijcnn64981.2025.11228519URL

Abstract

Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. However, FL models often suffer performance degradation due to distribution shifts between training and deployment. Test-Time Adaptation (TTA) offers a promising solution by allowing models to adapt using only test samples. However, existing TTA methods in FL face challenges such as computational overhead, privacy risks from feature sharing, and scalability concerns due to memory constraints. To address these limitations, we propose Federated Continual Test-Time Adaptation (FedCTTA), a privacy-preserving and computationally efficient framework for federated adaptation. Unlike prior methods that rely on sharing local feature statistics, FedCTTA avoids direct feature exchange by leveraging similarity-aware aggregation based on model output distributions over randomly generated noise samples. This approach ensures adaptive knowledge sharing while preserving data privacy. Furthermore, FedCTTA minimizes the entropy at each client for continual adaptation, enhancing the model’s confidence in evolving target distributions. Our method eliminates the need for server-side training during adaptation and maintains a constant memory footprint, making it scalable even as the number of clients or training rounds increases. Extensive experiments show that FedCTTA surpasses existing methods across diverse temporal and spatial heterogeneity scenarios.

Keywords

  • Federated learning
  • spatial heterogeneity
  • continual test-time adaptation
  • entropy minimization
  • privacy preservation
  • distribution shift
  • temporal heterogeneity

CCDS Authors

Rakibul Hasan Rajib
Rakibul Hasan Rajib

Rakibul Hasan Rajib

  • Alumni, CCDS

  • PhD Student

    University of Central Florida, USA

  • BSc

    Independent University, Bangladesh (IUB)

Published at

IJCNN, ICIP, Scientific Reports

MD Md Akil Raihan Iftee
Md Akil Raihan Iftee

MD Md Akil Raihan Iftee

  • Alumni, CCDS

  • PhD Student

    Rochester Institute of Technology, USA

  • BSc

    Khulna University of Engineering & Technology, Bangladesh

Published at

IJCNN, ICIP

Mir Sazzat Hossain
Mir Sazzat Hossain

Mir Sazzat Hossain

  • Research Assistant, CCDS

  • Bachelors

    Independent University, Bangladesh (IUB)

Published at

WACV, IJCNN, ICIP, Smart Agricultural Technology

Dr. Sajib Mistry
Dr Sajib Mistry

Dr. Sajib Mistry

  • Collaborator, AI/ML Wing

  • Ph.D Candidate

    Curtin University, Australia

Published at

IJCNN

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Prof. Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Prof. Amin Ahsan Ali, PhD

  • Director, AI/ML Wing
  • Supervisor, ComBio Wing

  • Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

References

  1. 1.Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2016). Deep Residual Learning for Image Recognition. , 770–778[10.1109/cvpr.2016.90]
  2. 2.Eric Tzeng, Judy Hoffman, Kate Saenko, Trevor Darrell. (2017). Adversarial Discriminative Domain Adaptation. , 2962–2971[10.1109/cvpr.2017.316]
  3. 3.Mei Wang, Weihong Deng. (2018). Deep visual domain adaptation: A survey. Neurocomputing, 312, 135–153[10.1016/j.neucom.2018.05.083]
  4. 4.Qinbin Li, Bingsheng He, Dawn Song. (2021). Model-Contrastive Federated Learning. , 10708–10717[10.1109/cvpr46437.2021.01057]
  5. 5.Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Fengqiu Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer. (2021). The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 8320–8329[10.1109/iccv48922.2021.00823]
  6. 6.Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, Yong Zhang. (2021). Personalized Cross-Silo Federated Learning on Non-IID Data. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 7865–7873[10.1609/aaai.v35i9.16960]
  7. 7.Qin Wang, Olga Fink, Luc Van Gool, Dengxin Dai. (2022). Continual Test-Time Domain Adaptation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7191–7201[10.1109/cvpr52688.2022.00706]
  8. 8.M. Jehanzeb Mirza, Jakub Micorek, Horst Possegger, Horst Bischof. (2022). The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14745–14755[10.1109/cvpr52688.2022.01435]
  9. 9.Hai Jin, Dongshan Bai, Dezhong Yao, Yutong Dai, Lin Gu, Yu Chen, Lichao Sun. (2022). Personalized Edge Intelligence via Federated Self-Knowledge Distillation. IEEE Transactions on Parallel and Distributed Systems, 34(2), 567–580[10.1109/tpds.2022.3225185]
  10. 10.Robert A. Marsden, Mario Döbler, Bin Yang. (2024). Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction. , 2543–2553[10.1109/wacv57701.2024.00254]
  11. 11.Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão, Mina Alibeigi, Jiajun Shen, Nicholas D. Lane. (2023). L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning. , 16418–16427[10.1109/iccv51070.2023.01509]
  12. 12.Max Klabunde, Tobias Schumacher, Markus Strohmaier, Florian Lemmerich. (2025). Similarity of Neural Network Models: A Survey of Functional and Representational Measures. ACM Computing Surveys, 57(9), 1–52[10.1145/3728458]
  13. 13.Jiayuan Zhang, Xuefeng Liu, Yukang Zhang, Guogang Zhu, Jianwei Niu, Shaojie Tang. (2024). Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning. , 4191–4202[10.1145/3637528.3671908]
  14. 14.Jian Xu, Shao‐Lun Huang. (2023). A Joint Training-Calibration Framework for Test-Time Personalization with Label Shift in Federated Learning. , 4370–4374[10.1145/3583780.3615173]

Cited By

  1. 1.Chen Zhang, Ge Su, Huaxiao Zhou, Lu Hao, Fenglin Zhu, Jintai Chen, Yuxiang Cai, Jianwei Yin, Hongxia Xu. (2026). Federated Learning Meets Test-Time Adaptation: Methods, Challenges, and Future Directions. International Journal of Computer Vision, 134(8)[10.1007/s11263-026-02937-6]
  2. 2.Pian Qi, Jingzhi Tu. (2026). FU-TTA: Federated Unlearning for Test-Time Adaptation. International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), 120–127[10.1109/mlise70044.2026.11607543]
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