Publications

Federated Learning

• 1. S. Ergisi, L. Maßny and R. Bitar, “ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning,” to be presented at IEEE International Conference on Federated Learning Technologies and Applications (FLTA), 2025

  2. Y. Xia, T. Jahani-Nezhad and R. Bitar, “Fed-DPRoc: Communication-Efficient Differentially Private and Robust Federated Learning,” to be presented at IEEE International Conference on Federated Learning Technologies and Applications (FLTA), 2025

  3. M. Egger and R. Bitar, “Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning,” IEEE Information Theory Workshop, 2025.

  4. M. Egger, R. L. Urbanke and R. Bitar, Federated One-Shot Learning with Data Privacy and Objective-Hiding, IEEE Transactions on Information Forensics and Security, Vol. 20, 2025.

  5. M. Egger, M. Bakshi, R. Bitar, Byzantine-Resilient Zero-Order Optimization for Scalable Federated Fine-Tuning of Large Language Models, ICML2025 ES-FoMo III Workshop, 2025.

  6. M. Egger, R. Bitar, A. Wachter-Zeh, N. Weinberger, D. Gündüz, BiCompFL: Bi-Directional Compression for Stochastic Federated Learning, ICML2025 ML4Wireless Workshop, 2025. 

  7. Y. Xia, M. Egger, C. Hofmeister and R. Bitar, LoByITFL: Low Communication Secure and Private Federated Learning, ACM ASIACCS Workshop on Secure and Efficient Federated Learning, 2025.

  8. M. Egger, R. Bitar, A. Wachter-Zeh, N. Weinberger and D. Gündüz, Multi-Terminal Remote Generation and Estimation Over a Broadcast Channel With Correlated Priors, IEEE ISIT, 2025.

  9. M. Egger, R. L. Urbanke and R. Bitar, Federated One-Shot Learning with Data Privacy and Objective-Hiding, IEEE ISIT, 2025.

  10. M. Egger, C. Hofmeister, C. Kaya, R. Bitar and A. Wachter-Zeh, Scalable and Reliable Over-the-Air Federated Edge Learning, IEEE Globecom 2024.

  11. Y. Xia, C. Hofmeister, M. Egger and R. Bitar, Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises, IEEE ITW, 2024.

  12. M. Egger, C. Hofmeister, A. Wachter-Zeh and R. Bitar, Private Aggregation in Wireless Federated Learning with Heterogeneous Clusters, IEEE International Symposium on Information Theory (ISIT), 2023.

Machine unlearning

1. M. Egger, R. Bitar and R. L. Urbanke, “Efficient Machine Unlearning by Model Splitting and Core Sample Selection,” IEEE Information Theory Workshop, 2025.

Decentralized Learning

1. M. Egger, S. Lage, R. Bitar and A. Wachter-Zeh, “Source Anonymity for Private Random Walk Decentralized Learning,” IEEE Information Theory Workshop, 2025.

2. M. Egger, G. Ayache, R. Bitar, A. Wachter-Zeh and S. El Rouayheb, Self-Duplicating Random Walks for Resilient Decentralized Learning on Graphs, IEEE Globecom 2024.

Distributed Learning

1. M. Egger, R. Bitar, A. Wachter-Zeh and D. Gündüz, Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits, Entropy, 2025.

2. L. Maßny, R. Bitar, F. Ye, S. El Rouayheb, Between Close Enough to Reveal and Far Enough to Protect: a New Privacy Region for Correlated Data, IEEE Information Theory Workshop, 2025.

3. M. Egger, S. Kas Hanna and R. Bitar, Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load, IEEE International Symposium on Information Theory (ISIT), 2023.

4. M. Egger, R. Bitar, A. Wachter-Zeh and D. Gündüz, Efficient Distributed Machine Learning via Combinatorial Multi-Armed Bandits, IEEE International Symposium on Information Theory (ISIT), 2022.

5. S. Kas Hanna, R. Bitar, P. Parag, V. Dasari and S. El Rouayheb, Adaptive Stochastic Gradient Descent for Fast and Communication-Efficient Distributed Learning, preprint, arXiv:2208.03134, 2022.

6. S. Kas Hanna, R. Bitar, P. Parag, V. Dasari and S. El Rouayheb, Adaptive Distributed Stochastic Gradient Descent for Minimizing Delay in the Presence of Stragglers, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, 2020.

7. R. Bitar, M. Wootters and S. El Rouayheb, Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning, IEEE Journal on Selected Areas in Information Theory,, Vol.1, No. 1, May 2020. (arXiv:1905.05383)

8. R. Bitar, M. Wootters and S. El Rouayheb, Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning, IEEE Information Theory Workshop (ITW), 2019.

Private and Secure Coded Computing

• 1. C. Hofmeister, R. Bitar, A. Wachter-Zeh, CAT and DOG: Improved Codes for Private Distributed Matrix Multiplication, preprint, arXiv:2501.12371, 2025.

  2. S. Jain, L. Maßny, C. Hofmeister, E. Yaakobi and R. Bitar, Interactive Byzantine-Resilient Gradient Coding for General Data Assignments, submitted to IEEE ISIT 2024.

  3. R. Bitar, M. Egger, A. Wachter-Zeh and M. Xhemrishi, Sparsity and Privacy in Secret Sharing: A Fundamental Trade-Off, IEEE Transactions on Information Forensics and Security, Vol. 19, April 2024. (arXiv:2308.06413)

  4. C. Hofmeister, L. Maßny, E. Yaakobi and R. Bitar, Byzantine-Resilient Gradient Coding through Local Gradient Computations, IEEE Transactions on Information Theory, early access, 2025.

  5. Z. Deng, V. Ramkumar, R. Bitar and N. Raviv, Private Inference in Quantized Models, IEEE Transactions on Information Theory, early access, 2025.

  6. C. Hofmeister, L. Maßny, E. Yaakobi and R. Bitar, Trading Communication for Computation in Byzantine-Resilient Gradient Coding, IEEE International Symposium on Information Theory (ISIT), 2023.

  7. M. Egger, M. Xhemrishi, A. Wachter-Zeh and R. Bitar, Sparse and Private Distributed Matrix Multiplication with Straggler Tolerance, IEEE International Symposium on Information Theory (ISIT), 2023.

  8. N. Raviv, R.Bitar and E. Yaakobi, Information Theoretic Private Inference in Quantized Models, IEEE International Symposium on Information Theory (ISIT), 2022.

  9. M. Xhemrishi, R. Bitar and A. Wachter-Zeh, Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees, IEEE International Symposium on Information Theory (ISIT), 2022.

  10. C. Hofmeister, R. Bitar, M. Xhemrishi and A. Wachter-Zeh, Secure Private and Adaptive Matrix Multiplication Beyond the Singleton Bound, IEEE Journal on Selected Areas in Information Theory (JSAIT), Vol. 3, No. 2, June 2022. (arXiv:2108.05742)

  11. R. Bitar, M. Xhemrishi and A. Wachter-Zeh, Adaptive Private Distributed Matrix Multiplication, IEEE Transactions on Information Theory, Vol. 68, No. 4, April 2022. (arXiv:2004.12925)

  12. C. Hofmeister, R. Bitar, M. Xhemrishi and A. Wachter-Zeh, Secure Private and Adaptive Matrix Multiplication Beyond the Singleton Bound, Twelfth International Workshop on Coding and Cryptography (WCC), March 2022.

  13. R. Bitar, Y. Xing, Y. Keshtkarjahromi, V. Dasari, S. El Rouayheb, and H. Seferoglu, Private and Rateless Adaptive Coded Computation at the Edge, EURASIP Journal on Wireless Communications and Networking, Vol.1, January 2021. (arXiv:1909.12611)

  14. R. Bitar, M. Xhemrishi and A. Wachter-Zeh, Fountain Codes for Private Distributed Matrix-Matrix Multiplication, IEEE International Symposium on Information Theory and its Applications (ISITA), 2020.

  15. R. Bitar, P. Parag and S. El Rouayheb, Minimizing Latency for Secure Coded Computing Using Secret Sharing via Staircase Codes, IEEE Transactions on Communication, Vol. 68, No. 8, August 2020. (arXiv:1802.02640)

  16. Y. Keshtkarjahromi, R. Bitar, V. Dasari, S. El Rouayheb, and H. Seferoglu, Secure Coded Cooperative Computation at the Heterogeneous Edge against Byzantine Attacks, preprint, arXiv:1908.05385, 2020.

  17. Y. Keshtkarjahromi, R. Bitar, V. Dasari, S. El Rouayheb, and H. Seferoglu, Secure Coded Cooperative Computation at the Heterogeneous Edge against Byzantine Attacks, IEEE Global Communication Conference (GLOBECOM), Waikoloa, 2019

  18. R. Bitar, Y. Xing, Y. Keshtkarjahromi, V. Dasari, S. El Rouayheb, and H. Seferoglu, PRAC: Private and Rateless Adaptive Coded Computation at the Edge, SPIE Defense + Commercial Sensing, Baltimore, 2019.

  19. E. Klarlund, R. Bitar and S. El Rouayheb, Search Efficient Blockchain-Based Immutable Logging And Querying, submitted to BMC Medical Genomics, 2018.

  20. R. Bitar, P. Parag and S. El Rouayheb, Minimizing Latency for Secure Distributed Computing, IEEE International Symposium on Information Theory (ISIT), Aachen, 2017.

  21. R. Bitar, Codes for Private Distributed Computation with Applications to Machine Learning, Rutgers University school of graduate studies electronic theses and dissertations, 2020.

Privacy and Security in Distributed Storage

• 1. T. Janz, H. Liu, R. Bitar and F. R. Kschischang, Secure Storage using Maximally Recoverable Locally Repairable Codes, accepted in IEEE ISIT 2024.

  2. M. Xhemrishi, M. Egger and R. Bitar, Efficient Private Storage of Sparse Machine Learning Data, IEEE Information Theory Workshop (ITW), invited paper, 2022.

  3. R. Bitar and S. Jaggi, Communication Efficient Secret Sharing in the Presence of Malicious Adversary, preprint, arXiv:2002.03374, 2020.

  4. R. Bitar and S. Jaggi, Communication Efficient Secret Sharing in the Presence of Malicious Adversary, IEEE International Symposium on Information Theory (ISIT), 2020.

  5. R. Bitar and S. El Rouayheb, Staircase Codes for Secret Sharing with Optimal Communication and Read Overheads, IEEE Transactions on Information Theory, Vol. 64, No. 2, February 2018. (arXiv:1512.02990)

  6. R. Bitar and S. El Rouayheb, Staircase-PIR: Universally Robust Private Information Retrieval, IEEE Information Theory Workshop (ITW), Guangzhou, 2018.

  7. R. Bitar and S. El Rouayheb, Staircase Codes for Secret Sharing with Optimal Communication and Read Overheads, IEEE International Symposium on Information Theory (ISIT), Barcelona, 2016.

  8. R. Bitar and S. El Rouayheb, Securing data against Limited-Knowledge Adversaries in Distributed Storage Systems, IEEE International Symposium on Information Theory (ISIT), Hong Kong, 2015.

DNA-based Data Storage

1. C. Hofmeister, A. Gruica, D. Hanania, R. Bitar and E. Yaakobi, Achieving DNA Labeling Capacity with Minimum Labels through Extremal de Bruijn Subgraphs, IEEE ISIT 2024.

Insertion/Deletion-Correcting Codes

1. L. Welter, R. Bitar, A. Wachter-Zeh and E. Yaakobi, Multiple Criss-Cross Deletion and Insertion Correcting Codes, IEEE Transactions on Information Theory, Vol. 68, No. 6, June, 2022. (arXiv:2102.02727)

2. E. Stylianou, L. Welter, R. Bitar, A. Wachter-Zeh and E. Yaakobi, Equivalence of Insertion/Deletion Correcting Codes for d-dimensional Arrays, IEEE International Symposium on Information Theory (ISIT), 2022.

3. S. Kas Hanna and R. Bitar, Codes for Detecting Deletions and Insertions in Concatenated Strings, preprint, arXiv:2105.00212, 2021.

4. S. Kas Hanna and R. Bitar, Codes for Detecting Deletions and Insertions in Concatenated Strings, IEEE International Symposium on Information Theory (ISIT), 2021.

5. R. Bitar, S. Kas Hanna, N. Polyanskii and I. Vorobyev, Optimal Codes Correcting Localized Deletions, IEEE International Symposium on Information Theory (ISIT), 2021.

6. L. Welter, R. Bitar, A. Wachter-Zeh and E. Yaakobi, Multiple Criss-Cross Deletion-Correcting Codes, IEEE International Symposium on Information Theory (ISIT), 2021.

7. R. Bitar, L. Welter, I. Smagloy, A. Wachter-Zeh and E. Yaakobi, Criss-Cross Insertion and Deletion Correcting Codes, IEEE Transactions on Information Theory, Vol.67, No.12, December 2021. (arXiv:2004.14740)

8. R. Bitar, I. Smagloy, L. Welter, A. Wachter-Zeh and E. Yaakobi, Criss-Cross Deletion Correcting Codes, IEEE International Symposium on Information Theory and its Applications (ISITA), 2020.

Function-Correcting Codes

• 1. A. Lenz, R. Bitar, A. Wachter-Zeh and E. Yaakobi, Function-Correcting Codes, IEEE Transactions on Information Theory, Early Access, 2023. (arXiv:2102.03094)

  2. A. Lenz, R. Bitar, A. Wachter-Zeh and E. Yaakobi, Function-Correcting Codes, IEEE International Symposium on Information Theory (ISIT), 2021.

Private and Secure Network Coding

1. S. Li, R. Bitar, S. Jaggi and Y. Zhang, Network Coding with Myopic Adversaries, IEEE Journal on Selected Areas in Information Theory (JSAIT), Vol. 2, No. 4, December 2021. (arXiv:2102.09885).

2. S. Li, R. Bitar, S. Jaggi and Y. Zhang, Network Coding with Myopic Adversaries, IEEE International Symposium on Information Theory (ISIT), 2021.