A lightweight blockchain-based defense method for federated self-supervised learning

In recent years, deep learning technologies have experienced significant advancements, especially in the computer vision area. However, their success depends heavily on the availability of vast amounts of labeled data, introducing several data-gathering issues. To cope with these limitations, we use Federated Self-Supervised Learning (FedSSL), a framework that integrates Self-Supervised Learning (SSL) with Federated Learning (FL). FedSSL uses unlabeled data to enhance model performance and generalization without compromising data privacy. Despite its advantages, our research reveals vulnerabilities in FedSSL, such as susceptibility to model poisoning attacks. We introduce a Blockchain-based defense method for FedSSL (BCH-FedSSL) to face this risk, which incorporates blockchain technology to decentralize model aggregation, ensuring data integrity and transparency. Experimental results conducted under IID data distributions using the CIFAR-10, CIFAR-100, and Fashion-MNIST datasets demonstrate that BCH-FedSSL's effectiveness in maintaining model accuracy and robustness under adversarial conditions. The proposed method achieved, in the presence of a poisoning attack, a 30 % performance improvement on CIFAR-10, a 27 % on CIFAR-100, and a 31 % on Fashion-MNIST. This study highlights the potential of combining blockchain with federated learning to create secure, scalable, and efficient decentralized learning systems.