10 papers
Trust-Aware Topology Learning for Dynamic Decentralized Federated Learning under Adversaries
Shubham Vaishnav, Murtaza Rangwala, Ali Beikmohammadi +3
In dynamic mobile decentralized federated learning (DFL), adversaries can poison both model updates and the topology information devices use to choose collaborators. We present DMT…
Topology-Aware Differential Privacy in Hierarchical Federated Learning
Murtaza Rangwala, Richard O. Sinnott, Rajkumar Buyya
Hierarchical federated learning places regional aggregators between clients and the cloud, so a participant's update is observed only alongside its neighbours'. The concealment thi…
SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening
Murtaza Rangwala, Farag Azzedin, Richard O. Sinnott +1
Decentralized Federated Learning (DFL) enables privacy-preserving collaborative training without centralized servers but remains vulnerable to Byzantine attacks. Existing Byzantine…
A Periodic Space of Distributed Computing: Vision & Framework
Mohsen Amini Salehi, Adel N. Tousi, Hai Duc Nguyen +5
Advances in networking and computing technologies throughout the early decades of the 21st century have transformed long-standing dreams of pervasive communication and computation…
Differential Privacy for Secure Machine Learning in Healthcare IoT-Cloud Systems
N Mangala, Murtaza Rangwala, S Aishwarya +5
Healthcare has become exceptionally sophisticated, as wearables and connected medical devices revolutionize remote patient monitoring, emergency response, medication management, di…
Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
Murtaza Rangwala, Richard O. Sinnott, Rajkumar Buyya
Decentralized federated learning (DFL) enables collaborative model training across edge devices without centralized coordination, offering resilience against single points of failu…