12 citations · 14 across the 23 of their papers we have counts for
3 papers · 1 filter
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…