12 citations · 12 across the 12 of their papers we have counts for
14 papers
Random Access Expectation in DNA Storage and Fountain Codes
Christoph Hofmeister, Rawad Bitar, Eitan Yaakobi
Motivated by DNA data storage, we study the expected number of coded symbols drawn from a linear code until a desired information symbol can be decoded - the random access expectat…
On the Extension of Private Distributed Matrix Multiplication Schemes to the Grid Partition
Christoph Hofmeister, Razane Tajeddine, Antonia Wachter-Zeh +1
We consider polynomial codes for private distributed matrix multiplication (PDMM/SDMM). Existing codes for PDMM are either specialized for the outer product partitioning (OPP), or…
Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning
Yue Xia, Christoph Hofmeister, Maximilian Egger +1
Federated learning (FL) shows great promise in large-scale machine learning but introduces new privacy and security challenges. We propose ByITFL and LoByITFL, two novel FL schemes…
CAT and DOG: Improved Codes for Private Distributed Matrix Multiplication
Christoph Hofmeister, Rawad Bitar, Antonia Wachter-Zeh
We present novel constructions of polynomial codes for private distributed matrix multiplication (PDMM/SDMM) using outer product partitioning (OPP). We extend the degree table fram…
Scalable and Reliable Over-the-Air Federated Edge Learning
Maximilian Egger, Christoph Hofmeister, Cem Kaya +2
Federated edge learning (FEEL) has emerged as a core paradigm for large-scale optimization. However, FEEL still suffers from a communication bottleneck due to the transmission of h…
LoByITFL: Low Communication Secure and Private Federated Learning
Yue Xia, Maximilian Egger, Christoph Hofmeister +1
Privacy of the clients' data and security against Byzantine clients are key challenges in Federated Learning (FL). Existing solutions to joint privacy and security incur sacrifices…
Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises
Yue Xia, Christoph Hofmeister, Maximilian Egger +1
Federated learning (FL) shows great promise in large scale machine learning, but brings new risks in terms of privacy and security. We propose ByITFL, a novel scheme for FL that pr…
Interactive Byzantine-Resilient Gradient Coding for General Data Assignments
Shreyas Jain, Luis Maßny, Christoph Hofmeister +2
We tackle the problem of Byzantine errors in distributed gradient descent within the Byzantine-resilient gradient coding framework. Our proposed solution can recover the exact full…
Byzantine-Resilient Gradient Coding through Local Gradient Computations
Christoph Hofmeister, Luis Maßny, Eitan Yaakobi +1
We consider gradient coding in the presence of an adversary controlling so-called malicious workers trying to corrupt the computations. Previous works propose the use of MDS codes…
Achieving DNA Labeling Capacity with Minimum Labels through Extremal de Bruijn Subgraphs
Christoph Hofmeister, Anina Gruica, Dganit Hanania +2
DNA labeling is a tool in molecular biology and biotechnology to visualize, detect, and study DNA at the molecular level. In this process, a DNA molecule is labeled by a set of spe…
Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion
Maximilian Egger, Christoph Hofmeister, Antonia Wachter-Zeh +1
In federated learning, a federator coordinates the training of a model, e.g., a neural network, on privately owned data held by several participating clients. The gradient descent…
Trading Communication for Computation in Byzantine-Resilient Gradient Coding
Christoph Hofmeister, Luis Maßny, Eitan Yaakobi +1
We consider gradient coding in the presence of an adversary, controlling so-called malicious workers trying to corrupt the computations. Previous works propose the use of MDS codes…
Nested Gradient Codes for Straggler Mitigation in Distributed Machine Learning
Luis Maßny, Christoph Hofmeister, Maximilian Egger +2
We consider distributed learning in the presence of slow and unresponsive worker nodes, referred to as stragglers. In order to mitigate the effect of stragglers, gradient coding re…
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…