Code and Data Repository for An Adaptive Federated Learning Algorithm with Attenuated Memory on Non-IID and Long-tail Data
Abstract
Addressing the dual challenges of privacy protection and data sharing in sectors such as finance and healthcare, this repository proposes AFLAM (Adaptive Federated Learning Algorithm with Attenuated Memory). By leveraging the decaying mechanism of gradient history to dynamically adjust client weights, this method effectively tackles data heterogeneity and significantly enhances the overall performance of the federated learning model.
Fields of Study & Subjects
Privacy-Preserving Technologies in Data
Big Data and Digital Economy
Advanced Data and IoT Technologies
Publication details
- Journal / source
- INFORMS journal on computing
- Publisher
- Institute for Operations Research and the Management Sciences (INFORMS)
- Publication date
- 10 February 2026