This paper presents a new privacy-preserving synthetic continual semantic segmentation framework for robotic surgery. The researchers address the problem of ‘catastrophic forgetting’ in Deep Neural Networks (DNNs), which refers to…
Continue readingThe article explores the concept of decentralized federated learning, a rapidly evolving method that offers privacy-preserving features. However, this method also provides new attack surfaces for malicious users, potentially threatening…
Continue readingThis paper proposes a system that utilizes Vehicle-to-Everything (V2X) technology for autonomous vehicles to exchange data and make real-time decisions. The system also operates a federated learning scheme to create…
Continue readingThis paper introduces Brave, a protocol that ensures Byzantine resilience and privacy-preserving property for Peer-to-Peer (P2P) Federated Learning (FL). In FL, multiple participants collaboratively train a global machine learning model…
Continue readingThis paper proposes a new Privacy-Preserving TFGC (Target Fine-grained Classification) Framework based on Federated Learning, named PRFL. The framework is designed to help clients learn global and local knowledge, improving…
Continue readingThe article discusses the BOOMERANG protocol, a new decentralized privacy-preserving incentive protocol. It leverages cryptographic black box accumulators to securely store user interactions within the incentive system. It uses zero-knowledge…
Continue readingThe paper presents a novel algorithm, Federated Learning with Accumulated Regularized Embeddings (FLARE), which addresses challenges in decentralized machine learning, specifically in federated learning (FL). The algorithm uses sparse training…
Continue readingThis research presents a novel image classification scheme that preserves privacy by using a feature extractor and an encoder to mask the plaintext image through a Noise-like Adversarial Example (NAE)….
Continue readingThe article discusses the issue of privacy in graph embedding, a powerful tool for learning latent representations for nodes in a graph. The authors highlight the need for privacy-preserving graph…
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