12 citations · 14 across the 23 of their papers we have counts for
8 papers · 1 filter
Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning
Yue Xia, Christoph Hofmeister, Maximilian Egger +1
Federated learning (FL) shows great promise in large-scale machine learning but introduces new privacy and security challenges. We propose ByITFL and LoByITFL, two novel FL schemes…
Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
Maximilian Egger, Rawad Bitar
Ensuring resilience to Byzantine clients while maintaining the privacy of the clients' data is a fundamental challenge in federated learning (FL). When the clients' data is homogen…
Efficient Machine Unlearning by Model Splitting and Core Sample Selection
Maximilian Egger, Rawad Bitar, Rüdiger Urbanke
Machine unlearning is essential for meeting legal obligations such as the right to be forgotten, which requires the removal of specific data from machine learning models upon reque…
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…
Source Anonymity for Private Random Walk Decentralized Learning
Maximilian Egger, Svenja Lage, Rawad Bitar +1
This paper considers random walk-based decentralized learning, where at each iteration of the learning process, one user updates the model and sends it to a randomly chosen neighbo…
Federated One-Shot Learning with Data Privacy and Objective-Hiding
Maximilian Egger, Rüdiger Urbanke, Rawad Bitar
Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the…
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…
Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning
Maximilian Egger, Mayank Bakshi, Rawad Bitar
We introduce CyBeR-0, a Byzantine-resilient federated zero-order optimization method that is robust under Byzantine attacks and provides significant savings in uplink and downlink…