7 citations · 11 across the 10 of their papers we have counts for
11 papers
Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
Marvin Xhemrishi, Alexandre Graell i Amat, Balázs Pejó
Federated learning with secure aggregation enables private and collaborative learning from decentralised data without leaking sensitive client information. However, secure aggregat…
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…
Sparsity and Privacy in Secret Sharing: A Fundamental Trade-Off
Rawad Bitar, Maximilian Egger, Antonia Wachter-Zeh +1
This work investigates the design of sparse secret sharing schemes that encode a sparse private matrix into sparse shares. This investigation is motivated by distributed computing,…
Sparse and Private Distributed Matrix Multiplication with Straggler Tolerance
Maximilian Egger, Marvin Xhemrishi, Antonia Wachter-Zeh +1
This paper considers the problem of outsourcing the multiplication of two private and sparse matrices to untrusted workers. Secret sharing schemes can be used to tolerate straggler…
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…
Efficient Private Storage of Sparse Machine Learning Data
Marvin Xhemrishi, Maximilian Egger, Rawad Bitar
We consider the problem of maintaining sparsity in private distributed storage of confidential machine learning data. In many applications, e.g., face recognition, the data used in…
Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees
Marvin Xhemrishi, Rawad Bitar, Antonia Wachter-Zeh
We consider the problem of designing a coding scheme that allows both sparsity and privacy for distributed matrix-vector multiplication. Perfect information-theoretic privacy requi…
Computational Code-Based Privacy in Coded Federated Learning
Marvin Xhemrishi, Alexandre Graell i Amat, Eirik Rosnes +1
We propose a privacy-preserving federated learning (FL) scheme that is resilient against straggling devices. An adaptive scenario is suggested where the slower devices share their…
Secure Private and Adaptive Matrix Multiplication Beyond the Singleton Bound
Christoph Hofmeister, Rawad Bitar, Marvin Xhemrishi +1
We consider the problem of designing secure and private codes for distributed matrix-matrix multiplication. A master server owns two private matrices and hires worker nodes to help…
Adaptive Private Distributed Matrix Multiplication
Rawad Bitar, Marvin Xhemrishi, Antonia Wachter-Zeh
We consider the problem of designing codes with flexible rate (referred to as rateless codes), for private distributed matrix-matrix multiplication. A master server owns two privat…
Rateless Codes for Private Distributed Matrix-Matrix Multiplication
Rawad Bitar, Marvin Xhemrishi, Antonia Wachter-Zeh
We consider the problem of designing rateless coded private distributed matrix-matrix multiplication. A master server owns two private matrices and and wa…