67 citations · 262 across the 80 of their papers we have counts for
4 papers · 1 filter
BICompFL: Stochastic Federated Learning with Bi-Directional Compression
Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh +2
We address the prominent communication bottleneck in federated learning (FL). We specifically consider stochastic FL, in which models or compressed model updates are specified by d…
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…
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…
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
Marvin Xhemrishi, Johan Östman, Antonia Wachter-Zeh +1
We propose FedGT, a novel framework for identifying malicious clients in federated learning with secure aggregation. Inspired by group testing, the framework leverages overlapping…