Intervention & Interaction Federated Abnormality Detection with Noisy Clients

Abstract

Federated learning (FL), which trains a shared global model by collaboration between distributed clients (e.g. medical institutions) and preserves the privacy of local data, has been widely deployed in the medical field to benefit abnormality diagnosis. However, it is inevitable that local data contains noise across clients, resulting in notably performance deterioration in the global model. To this end, a practical yet challenging FL problem is studied in this paper, namely Federated abnormality detection with noisy clients (FADN). We represent the first effort to reason the FADN task as a structural causal model, and identify the main issue that leads to the performance deterioration, namely recognition bias. To tackle the problem, an Intervention & Interaction FL framework (FedInI) is proposed, comprising two key strategies: (1) Intervention: considering the data distribution heterogeneity caused by different noisy levels within each client, we use the global model to intervene the training of local models, by shuffling and mixing features extracted from different models and suppress the noise gradually; (2) Interaction: we devise an adaptive sample-wise weighting strategy that jointly considers the local training statuses and global noisy levels with a shared interactive layer. Extensive experiments on class-conditional noise and instance-dependant noise settings are conducted, FedInI outperforms state-of-the-arts by a remarkable margin. Code is available at github.com/CityU-AIM-Group/FedInI.