3 citations · 3 across the 10 of their papers we have counts for
11 papers
Private and Robust Contribution Evaluation in Federated Learning
Delio Jaramillo Velez, Gergely Biczok, Alexandre Graell i Amat +2
Cross-silo federated learning allows multiple organizations to collaboratively train machine learning models without sharing raw data, but client updates can still leak sensitive i…
Perfectly-Private Analog Secure Aggregation in Federated Learning
Delio Jaramillo-Velez, Charul Rajput, Ragnar Freij-Hollanti +2
In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model. To address the risk o…
Practical Bayes-Optimal Membership Inference Attacks
Marcus Lassila, Johan Östman, Khac-Hoang Ngo +1
We develop practical and theoretically grounded membership inference attacks (MIAs) against both independent and identically distributed (i.i.d.) data and graph-structured data. Bu…
Soft-Decision Decoding for LDPC Code-Based Quantitative Group Testing
Marvin Xhemrishi, Johan Östman, Alexandre Graell i Amat
We consider the problem of identifying defective items in a population with non-adaptive quantitative group testing. For this scenario, Mashauri et al. recently proposed a low-dens…
On Local Mutual-Information Privacy
Khac-Hoang Ngo, Johan Östman, Alexandre Graell i Amat
Local mutual-information privacy (LMIP) is a privacy notion that aims to quantify the reduction of uncertainty about the input data when the output of a privacy-preserving mechanis…
Timely Status Updates in Slotted ALOHA Networks With Energy Harvesting
Khac-Hoang Ngo, Giuseppe Durisi, Andrea Munari +2
We investigate the age of information (AoI) in a scenario where energy-harvesting devices send status updates to a gateway following the slotted ALOHA protocol and receive no feedb…
Secure Aggregation is Not Private Against Membership Inference Attacks
Khac-Hoang Ngo, Johan Östman, Giuseppe Durisi +1
Secure aggregation (SecAgg) is a commonly-used privacy-enhancing mechanism in federated learning, affording the server access only to the aggregate of model updates while safeguard…
Decoupled Subgraph Federated Learning
Javad Aliakbari, Johan Östman, Alexandre Graell i Amat
We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subg…
Age of Information in Slotted ALOHA With Energy Harvesting
Khac-Hoang Ngo, Giuseppe Durisi, Alexandre Graell i Amat +2
We examine the age of information (AoI) of a status update system that incorporates energy harvesting and uses the slotted ALOHA protocol. We derive analytically the average AoI an…
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
Marvin Xhemrishi, Johan Östman, Antonia Wachter-Zeh +1
We propose FedGT, a novel framework for identifying malicious clients in federated learning with secure aggregation. Inspired by group testing, the framework leverages overlapping…
Unsourced Multiple Access with Common Alarm Messages: Network Slicing for Massive and Critical IoT
Khac-Hoang Ngo, Giuseppe Durisi, Alexandre Graell i Amat +3
We investigate the coexistence of massive and critical Internet of Things (IoT) services in the context of the unsourced multiple access (UMA) framework introduced by Polyanskiy (2…