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Full-Text Articles in Physical Sciences and Mathematics
Fedvcp: A Federated-Learning-Based Cooperative Positioning Scheme For Social Internet Of Vehicles, Xiangjie Kong, Haoran Gao, Guojiang Shen, Gaohui Duan, Sajal K. Das
Fedvcp: A Federated-Learning-Based Cooperative Positioning Scheme For Social Internet Of Vehicles, Xiangjie Kong, Haoran Gao, Guojiang Shen, Gaohui Duan, Sajal K. Das
Computer Science Faculty Research & Creative Works
Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes …
Securing Federated Learning Against Overwhelming Collusive Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das
Securing Federated Learning Against Overwhelming Collusive Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das
Computer Science Faculty Research & Creative Works
In the era of a data-driven society with the ubiquity of Internet of Things (IoT) devices storing large amounts of data localized at different places, distributed learning has gained a lot of traction, however, assuming independent and identically distributed data (iid) across the devices. While relaxing this assumption that anyway does not hold in reality due to the heterogeneous nature of devices, federated learning (FL) has emerged as a privacy-preserving solution to train a collaborative model over non-iid data distributed across a massive number of devices. However, the appearance of malicious devices (attackers), who intend to corrupt the FL model, …
Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das
Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated learning distributes model training among multiple clients who, driven by privacy concerns, perform training using their local data and only share model weights for iterative aggregation on the server. In this work, we explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. Finally, we validate the effectiveness of our algorithm in presence of varying number of attackers on a classification task using a well-known Fashion-MNIST dataset.