Semi-Supervised Federated Peer Learning for Skin Lesion Classification

TL;DR

This paper introduces FedPerl, a semi-supervised federated learning approach inspired by peer learning, which improves skin lesion classification accuracy while preserving privacy and reducing communication costs.

Contribution

FedPerl is a novel semi-supervised federated learning method that leverages peer learning and anonymization to enhance skin lesion classification with limited labeled data.

Findings

FedPerl achieves comparable accuracy to state-of-the-art methods with few labeled samples.

It outperforms existing semi-supervised federated learning methods by 15.8%.

The method generalizes well to unseen clients and is robust to noisy data.

Abstract

Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and mortality rate substantially. The recent improvement in automated cancer classification using deep learning methods has reached a human-level performance requiring a large amount of annotated data assembled in one location, yet, finding such conditions usually is not feasible. Recently, federated learning (FL) has been proposed to train decentralized models in a privacy-preserved fashion depending on labeled data at the client-side, which is usually not available and costly. To address this, we propose \verb!FedPerl!, a semi-supervised federated learning method. Our method is inspired by peer learning from educational psychology and ensemble averaging from committee machines. FedPerl builds communities based on clients' similarities. Then it encourages communities members to learn from each other to generate more accurate pseudo labels for the unlabeled data. We also proposed the peer anonymization (PA) technique to anonymize clients. As a core component of our method, PA is orthogonal to other methods without additional complexity and reduces the communication cost while enhancing performance. Finally, we propose a dynamic peer-learning policy that controls the learning stream to avoid any degradation in the performance, especially for individual clients. Our experimental setup consists of 71,000 skin lesion images collected from 5 publicly available datasets. We test our method in four different scenarios in SSFL. With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1.8% and 15.8%, respectively. Also, it generalizes better to unseen clients while being less sensitive to noisy ones.