Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data

Abstract

Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility because data is not independent and identically distributed (non-IID). Recent work has proposed to cluster clients according to dataset fingerprints to improve model utility in such situations. These fingerprints aim to capture the key characteristics of clients’ local data distributions. Recently, a mechanism was proposed to calculate dataset fingerprints from raw client data. We find that this fingerprinting mechanism comes with substantial time and memory consumption, limiting its practical use to small datasets. Additionally, shared raw data fingerprints can directly leak sensitive visual information, in certain cases even resembling the original client training data. To alleviate these problems, we propose a Feature-based dataset FingerPrinting mechanism (FFP). We use the MedMNIST database to develop a highly realistic case study for FL on medical image data. Compared to existing methods, our proposed FFP reduces the computational overhead of fingerprint calculation while achieving similar model utility. Furthermore, FFP mitigates the risk of raw data leakage from fingerprints by design.