DecoyTrace: Toxic Decoys for Active Defense in Decentralized Federated Learning

Decentralized Federated Learning DFL eliminates the central aggregation server, reducing the single point of observation that traditional defenses against attacks rely on. As a result, peer-to-peer networks become exposed to malicious updates containing backdoors or semantic poisoning, since such...

A Crowdsensing Intrusion Detection Dataset for Decentralized Federated Learning Models

This paper introduces a dataset and experimental study for decentralized federated learning DFL applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls,...

Poster: FedBlockParadox -- a Framework for Simulating and Securing Decentralized Federated Learning

A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency offered by blockchain-based systems. While these approaches are promising, they often lack flexible tools to evaluat...