On homomorphic encryption based strategies for class imbalance in federated learning
Abstrak
Abstract Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. While pre-processing of training datasets is an efficient way to address both these issues in centralized learning environments, it is challenging to detect and address these issues in a distributed learning environment such as federated learning. In this paper, we propose FLICKER, a privacy preserving framework to address issues related to global class imbalance in federated learning. At the heart of our contribution lies the popular Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme, which is used by the clients to privately share their data attributes, and subsequently balance their datasets before implementing the federated learning scheme. Extensive experimental results show that our proposed method improves the federated learning accuracy numbers by up to 8 $$\%$$ when used along with popular datasets and relevant baselines.
Topik & Kata Kunci
Penulis (4)
Arpit Guleria
Harshan Jagadeesh
Ranjitha Prasad
B. N. Bharath
Akses Cepat
- Tahun Terbit
- 2025
- Sumber Database
- DOAJ
- DOI
- 10.1007/s44248-025-00095-7
- Akses
- Open Access ✓