Center for Secure and Intelligent Critical Systems (CSICS) Publications

Shard-Unlearn: A Sharded Elastic SGD Privacy Preserving Federated Unlearning Framework for 5G-Assisted Healthcare

ORCID

0000-0001-6455-5134 (Banerjee), 0000-0001-9199-2479 (Rana), 0000-0002-8789-0610 (Shetty)

Document Type

Article

Publication Date

2025

DOI

10.1609/aaaiss.v7i1.36921

Publication Title

Proceedings of the AAAI Symposium Series

Volume

7

Issue

1

Pages

481-487

Abstract

Smart healthcare systems are generating unprecedented volumes of sensitive data, making robust privacy preservation a critical requirement. Traditional machine unlearning (MU) techniques aims to excise specific data points and their statistical influence from trained machine learning (ML) model. Thus, they suffer from limited computational efficiency, poor scalability, and suboptimal model convergence when applied to largescale, big-data (BD) healthcare environments. These limitations become even more significant in 5G-assisted settings, where real-time connectivity and rapid data processing are essential. To address these challenges, we introduce the concept of data sharding which partitions healthcare datasets into manageable segments. In the paper, we introduce Shard-Unlearn framework, that implements federated unlearning (FU) process to the shards that contain sensitive data. This reduces the overall computational overhead and optimizes model convergence over 5G networks. In the framework, we present the elastic stochastic gradient descent (SGD) optimization which effectively remove the targeted data and associated statistical perturbations from the local models. The framework is tested over the ADMISSIONS benchmark dataset, which is divided 10 shards. The framework is compared on computational efficiency, model robustness, and privacy preservation metrics. Statistical findings reveals a 47.14% improvement in unlearning impact (as measured by recall) while striking a balanced trade-off between performance and data security. These results underscore the viability of the framework as a scalable and privacy-preserving solution formodern 5G-assisted healthcare systems.

Rights

© 2023, Association for the Advancement of Artificial Intelligence. All rights reserved.

"In the Returned Rights section of the AAAI copyright form, authors are specifically granted back the right to use their own papers for noncommercial uses, such as inclusion in their dissertations or the right to deposit their own papers in their institutional repositories, provided there is proper attribution. The published version is not available for posting outside the AAAI Digital Library."

Original Publication Citation

Chatterjee, S., Jain, S., Bhattacharya, P., Roy, S., Banerjee, S., Rana, P., & Shetty, S. (2025). Shard-Unlearn: A sharded elastic SGD privacy preserving federated unlearning framework for 5G-assisted healthcare. Proceedings of the AAAI Symposium Series, 7(1), 481-487. https://doi.org/10.1609/aaaiss.v7i1.36921

Link to Full Text

Share

COinS