77 citations · 179 across the 54 of their papers we have counts for
11 papers · 1 filter
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…
Self-Regulating Random Walks for Resilient Decentralized Learning on Graphs
Maximilian Egger, Rawad Bitar, Ghadir Ayache +2
Consider the setting of multiple random walks (RWs) on a graph executing a certain computational task. For instance, in decentralized learning via RWs, a model is updated at each i…
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…
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization
Afonso de Sá Delgado Neto, Maximilian Egger, Mayank Bakshi +1
We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-communication efficient Federated Learning, resilient to Byzantine faults. We show through extensiv…
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…