77 citations · 179 across the 54 of their papers we have counts for
12 papers · 1 filter
ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning
Sena Ergisi, Luis Maßny, Rawad Bitar
Federated Learning (FL) emerged as a widely studied paradigm for distributed learning. Despite its many advantages, FL remains vulnerable to adversarial attacks, especially under d…
Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning
Yue Xia, Tayyebeh Jahani-Nezhad, Rawad Bitar
We propose Fed-DPRoC, a novel federated learning framework designed to jointly provide differential privacy (DP), Byzantine robustness, and communication efficiency. Central to our…
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…