12 citations · 14 across the 23 of their papers we have counts for
10 papers · 1 filter
Multi-Terminal Remote Generation and Estimation Over a Broadcast Channel With Correlated Priors
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh +2
We study the multi-terminal remote estimation problem under a rate constraint, in which the goal of the encoder is to help each decoder estimate a function over a certain distribut…
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…
Capacity-Maximizing Input Symbol Selection for Discrete Memoryless Channels
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh +2
Motivated by communication systems with constrained complexity, we consider the problem of input symbol selection for discrete memoryless channels (DMCs). Given a DMC, the goal is…
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…
Maximal-Capacity Discrete Memoryless Channel Identification
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh +2
The problem of identifying the channel with the highest capacity among several discrete memoryless channels (DMCs) is considered. The problem is cast as a pure-exploration multi-ar…
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…
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…
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…
Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh +1
We consider the distributed SGD problem, where a main node distributes gradient calculations among workers. By assigning tasks to all the workers and waiting only for the f…