Rakibul Hasan Rajib
- Research Assistant, CCDS
Research Interests
Computer Vision, Multimodal Learning, Federated Learning
Conference Paper2025Cited by 1
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
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
ScalabilityAdaptation (eye)Federated learningFeature (linguistics)Raw dataEntropy (arrow of time)Training setInformation privacy
CCDS Authors
MD Akil Raihan Iftee
- Research Assistant, CCDS
Research Interests
Multimodal Learning, Computer Vision, Trustworthy ML
Mir Sazzat Hossain
- Research Assistant, CCDS
Bachelors
Independent University, Bangladesh
Research Interests
Machine Learning, Computer Vision
Dr. Sajib Mistry
- Collaborator, AI/ML Wing
Ph.D Candidate
Curtin University, Australia
Research Interests
Service Computing, Cloud/Edge Computing, Machine Learning, Augmented Reality, Internet of Things
M Ashraful Amin, PhD
- Founder Director, CCDS
- Director, AI/ML, HCI & DS Wings
Research Interests
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics
Amin Ahsan Ali, PhD
- Director, AI/ML Wing
Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Research Interests
Artificial Intelligence, Machine Learning
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Cited By
- 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]