Towards Decentralized Deep Learning with Differential Privacy. - Hsin-Pai Cheng; Patrick Yu; Haojing Hu; Syed Zawad; Feng Yan 001; Shiyu Li 001; Hai Helen Li; Yiran Chen 001 (CLOUD, 2019)

Swift machine learning model serving scheduling: a region based reinforcement learning approach. - Heyang Qin; Syed Zawad; Yanqi Zhou; Lei Yang 001; Dongfang Zhao 001; Feng Yan 001 (SC, 2019)

EPNAS: Efficient Progressive Neural Architecture Search. - Yanqi Zhou; Peng Wang; Sercan Ömer Arik; Haonan Yu; Syed Zawad; Feng Yan 001; Greg Diamos (CoRR, 2019)

TiFL: A Tier-based Federated Learning System. - Zheng Chai; Ahsan Ali; Syed Zawad; Stacey Truex; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Heiko Ludwig; Feng Yan 001; Yue Cheng 001 (HPDC, 2020)

Reinforcement-Learning-Empowered MLaaS Scheduling for Serving Intelligent Internet of Things. - Heyang Qin; Syed Zawad; Yanqi Zhou; Sanjay Padhi; Lei Yang 001; Feng Yan 001 (IEEE Internet Things J., 2020)

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. - Syed Zawad; Ahsan Ali; Pin-Yu Chen; Ali Anwar 001; Yi Zhou 015; Nathalie Baracaldo; Yuan Tian 001; Feng Yan 001 (AAAI, 2021)

Introduction to Federated Learning Systems. - Syed Zawad; Feng Yan 001; Ali Anwar 001 (Federated Learning, 2022)

Local Training and Scalability of Federated Learning Systems. - Syed Zawad; Feng Yan 001; Ali Anwar 001 (Federated Learning, 2022)

Straggler Management. - Syed Zawad; Feng Yan 001; Ali Anwar 001 (Federated Learning, 2022)

Systems Bias in Federated Learning. - Syed Zawad; Feng Yan 001; Ali Anwar 001 (Federated Learning, 2022)

TIFF: Tokenized Incentive for Federated Learning. - Jingoo Han; Ahmad Faraz Khan 001; Syed Zawad; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Feng Yan 001; Ali Raza Butt (CLOUD, 2022)

Heterogeneity-Aware Adaptive Federated Learning Scheduling. - Jingoo Han; Ahmad Faraz Khan 001; Syed Zawad; Ali Anwar 001; Nathalie Baracaldo; Yi Zhou 015; Feng Yan 001; Ali Raza Butt (IEEE Big Data, 2022)

SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training. - Ahsan Ali; Syed Zawad; Paarijaat Aditya; Istemi Ekin Akkus; Ruichuan Chen; Feng Yan 001 (CoRR, 2022)

HDFL: A Heterogeneity and Client Dropout-Aware Federated Learning Framework. - Syed Zawad; Ali Anwar 001; Yi Zhou 015; Nathalie Baracaldo; Feng Yan 001 (CCGrid, 2023)

DySR: Adaptive Super-Resolution via Algorithm and System Co-design. - Syed Zawad; Cheng Li 001; Zhewei Yao; Elton Zheng; Yuxiong He; Feng Yan 001 (ICLR, 2023)

Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach. - Seyed Mahmoud Sajjadi Mohammadabadi; Syed Zawad; Feng Yan 001; Lei Yang 001 (CoRR, 2023)

Towards Scalable, Private and Practical Deep Learning. - Syed Zawad (2023)

AI augmented Edge and Fog computing: Trends and challenges. - Shreshth Tuli; Fatemeh Mirhakimi; Samodha Pallewatta; Syed Zawad; Giuliano Casale; Bahman Javadi; Feng Yan 001; Rajkumar Buyya; Nicholas R. Jennings (J. Netw. Comput. Appl., 2023)

Towards Collecting Royalties for Copyrighted Data for Generative Models. - Heiko Ludwig; Yi Zhou 015; Syed Zawad; Yuya Jeremy Ong; Pengyuan Li 001; Eric Butler; Eelaaf Zahid (ICWS, 2024)

Granite Code Models: A Family of Open Foundation Models for Code Intelligence. - Mayank Mishra; Matt Stallone; Gaoyuan Zhang; Yikang Shen; Aditya Prasad; Adriana Meza Soria; Michele Merler; Parameswaran Selvam; Saptha Surendran; Shivdeep Singh; Manish Sethi; Xuan-Hong Dang; et al. (CoRR, 2024)

Enabling scalable and adaptive machine learning training via serverless computing on public cloud. - Ahsan Ali; Xiaolong Ma; Syed Zawad; Paarijaat Aditya; Istemi Ekin Akkus; Ruichuan Chen; Lei Yang 001; Feng Yan 001 (Perform. Evaluation, 2025)

FedCust: Offloading hyperparameter customization for federated learning. - Syed Zawad; Xiaolong Ma; Jun Yi; Cheng Li 001; Minjia Zhang; Lei Yang 001; Feng Yan 001; Yuxiong He (Perform. Evaluation, 2025)

Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence. - Leonid Karlinsky; Assaf Arbelle; Abraham Daniels; Ahmed Nassar; Amit Alfassi; Bo Wu 018; Eli Schwartz; Dhiraj Joshi; Jovana Kondic; Nimrod Shabtay; Pengyuan Li 001; Roei Herzig; et al. (CoRR, 2025)

GneissWeb: Preparing High Quality Data for LLMs at Scale. - Hajar Emami-Gohari; Swanand Ravindra Kadhe; Syed Yousaf Shah; Constantin Adam; Abdulhamid Adebayo; Praneet Adusumilli; Farhan Ahmed; Nathalie Baracaldo Angel; Santosh Borse; Yuan Chi Chang; Xuan-Hong Dang; Nirmit Desai; et al. (CoRR, 2025)

SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging. - Aladin Djuhera; Swanand Ravindra Kadhe; Farhan Ahmed; Syed Zawad; Holger Boche (CoRR, 2025)

Speed Up Federated Learning in Heterogeneous Environments: A Dynamic Tiering Approach. - Seyed Mahmoud Sajjadi Mohammadabadi; Syed Zawad; Feng Yan 001; Lei Yang 001 (IEEE Internet Things J., 2025)

SafeCOMM: What about Safety Alignment in Fine-Tuned Telecom Large Language Models? - Aladin Djuhera; Swanand Ravindra Kadhe; Farhan Ahmed; Syed Zawad; Holger Boche; Walid Saad (CoRR, 2025)

Fixing It in Post: A Comparative Study of LLM Post-Training Data Quality and Model Performance. - Aladin Djuhera; Swanand Ravindra Kadhe; Syed Zawad; Farhan Ahmed; Heiko Ludwig; Holger Boche (CoRR, 2025)