6 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…
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…
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…