A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing
This addresses privacy concerns in federated learning for image classification, though it appears incremental as it builds on existing FL frameworks with a novel input-level approach.
The paper tackles privacy protection in federated learning for image classification by proposing Granular-Ball Federated Learning (GrBFL), which segments images into coarse-grained regions and reconstructs them into a graph, resulting in improved privacy, efficiency, and utility compared to state-of-the-art methods.
Federated Learning (FL) facilitates collaborative model training while prioritizing privacy by avoiding direct data sharing. However, most existing articles attempt to address challenges within the model's internal parameters and corresponding outputs, while neglecting to solve them at the input level. To address this gap, we propose a novel framework called Granular-Ball Federated Learning (GrBFL) for image classification. GrBFL diverges from traditional methods that rely on the finest-grained input data. Instead, it segments images into multiple regions with optimal coarse granularity, which are then reconstructed into a graph structure. We designed a two-dimensional binary search segmentation algorithm based on variance constraints for GrBFL, which effectively removes redundant information while preserving key representative features. Extensive theoretical analysis and experiments demonstrate that GrBFL not only safeguards privacy and enhances efficiency but also maintains robust utility, consistently outperforming other state-of-the-art FL methods. The code is available at https://github.com/AIGNLAI/GrBFL.