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The rise of distributed systems and pervasive computing has transformed how devices, systems, and services interact, creating a highly interconnected ecosystem. As the scale and complexity of these systems grow, issues related to security, privacy, and efficient management of decentralized resources become more pronounced. Addressing these challenges is critical to ensuring the security and privacy of user data, as well as compliance with regulatory standards in sensitive domains such as healthcare, finance, and beyond.
Federated Learning, a cutting-edge distributed learning paradigm, has emerged as a powerful tool for decentralized model training, enabling data privacy and reducing the need for central data aggregation. This method is pivotal in scenarios where data security and privacy are paramount.
Furthermore, Federated Unlearning—an innovative approach in the privacy-preserving space—focuses on the selective removal of data from models, which is essential for complying with data protection regulations and adapting to evolving privacy laws.
This special issue invites original contributions that explore the advanced technologies in Federated Learning and Unlearning, with an emphasis on their applications in security-sensitive environments. We welcome research papers that present novel methodologies, architectures, and strategies for enhancing the security, privacy, and efficiency of federated systems. All submissions will undergo peer review to ensure relevance and quality in line with the theme of this special issue.
For author information and guidelines on submission criteria, please visit the Author Information Page. Please submit papers through the IEEE Author Portal, and be sure to select the special-issue name. Manuscripts should be written in English and describe original research. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, The review process will comply with the standard review process of the IEEE Transactions on Dependable and Secure Computing journal. All submitted papers will be evaluated on the basis of relevance, significance of contribution, and technical quality.
In addition to submitting your paper to IEEE Transactions on Dependable and Secure Computing, you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE's data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission. Thank you!
