Nested Gradient Codes for Straggler Mitigation in Distributed Machine Learning. - Luis Maßny; Christoph Hofmeister; Maximilian Egger; Rawad Bitar; Antonia Wachter-Zeh (CoRR, 2022)

Secure Private and Adaptive Matrix Multiplication Beyond the Singleton Bound. - Christoph Hofmeister; Rawad Bitar; Marvin Xhemrishi; Antonia Wachter-Zeh (IEEE J. Sel. Areas Inf. Theory, 2022)

Private Aggregation in Wireless Federated Learning with Heterogeneous Clusters. - Maximilian Egger; Christoph Hofmeister; Antonia Wachter-Zeh; Rawad Bitar (ISIT, 2023)

Trading Communication for Computation in Byzantine-Resilient Gradient Coding. - Christoph Hofmeister; Luis Maßny; Eitan Yaakobi; Rawad Bitar (ISIT, 2023)

Scalable and Reliable Over-the-Air Federated Edge Learning. - Maximilian Egger; Christoph Hofmeister; Cem Kaya; Rawad Bitar; Antonia Wachter-Zeh (GLOBECOM, 2024)

Achieving DNA Labeling Capacity with Minimum Labels through Extremal de Bruijn Subgraphs. - Christoph Hofmeister; Anina Gruica; Dganit Hanania; Rawad Bitar; Eitan Yaakobi (ISIT, 2024)

Interactive Byzantine-Resilient Gradient Coding for General Data Assignments. - Shreyas Jain; Luis Maßny; Christoph Hofmeister; Eitan Yaakobi; Rawad Bitar (ISIT, 2024)

Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises. - Yue Xia; Christoph Hofmeister; Maximilian Egger; Rawad Bitar (ITW, 2024)

LoByITFL: Low Communication Secure and Private Federated Learning. - Yue Xia; Christoph Hofmeister; Maximilian Egger; Rawad Bitar (CoRR, 2024)

CAT and DOG: Improved Codes for Private Distributed Matrix Multiplication. - Christoph Hofmeister; Rawad Bitar; Antonia Wachter-Zeh (CoRR, 2025)

Byzantine-Resilient Gradient Coding Through Local Gradient Computations. - Christoph Hofmeister; Luis Maßny; Eitan Yaakobi; Rawad Bitar (IEEE Trans. Inf. Theory, 2025)

Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning. - Yue Xia; Christoph Hofmeister; Maximilian Egger; Rawad Bitar (CoRR, 2025)