77 citations · 179 across the 54 of their papers we have counts for
9 papers · 2 filters
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…
Secure Storage using Maximally Recoverable Locally Repairable Codes
Tim Janz, Hedongliang Liu, Rawad Bitar +1
This paper considers data secrecy in distributed storage systems (DSSs) using maximally recoverable locally repairable codes (MR-LRCs). Conventional MR-LRCs are in general not secu…
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…