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Syed Zawad
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2020 – today
- 2024
- [c9]Heiko Ludwig, Yi Zhou, Syed Zawad, Yuya Jeremy Ong, Pengyuan Li, Eric Butler, Eelaaf Zahid:
Towards Collecting Royalties for Copyrighted Data for Generative Models. ICWS 2024: 20-26 - [i7]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, Pengyuan Li, Kun-Lung Wu, Syed Zawad, Andrew Coleman, Matthew White, Mark Lewis, Raju Pavuluri, Yan Koyfman, Boris Lublinsky, Maximilien de Bayser, Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Yi Zhou, Chris Johnson, Aanchal Goyal, Hima Patel, S. Yousaf Shah, Petros Zerfos, Heiko Ludwig, Asim Munawar, Maxwell Crouse, Pavan Kapanipathi, Shweta Salaria, Bob Calio, Sophia Wen, Seetharami Seelam, Brian Belgodere, Carlos A. Fonseca, Amith Singhee, Nirmit Desai, David D. Cox, Ruchir Puri, Rameswar Panda:
Granite Code Models: A Family of Open Foundation Models for Code Intelligence. CoRR abs/2405.04324 (2024) - 2023
- [b1]Syed Zawad:
Towards Scalable, Private and Practical Deep Learning. University of Nevada, Reno, USA, 2023 - [j2]Shreshth Tuli
, Fatemeh Mirhakimi, Samodha Pallewatta, Syed Zawad, Giuliano Casale, Bahman Javadi
AI augmented Edge and Fog computing: Trends and challenges. J. Netw. Comput. Appl. 216: 103648 (2023) - [c8]Syed Zawad, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Feng Yan:
HDFL: A Heterogeneity and Client Dropout-Aware Federated Learning Framework. CCGrid 2023: 311-321 - [c7]Syed Zawad, Cheng Li, Zhewei Yao, Elton Zheng, Yuxiong He, Feng Yan:
DySR: Adaptive Super-Resolution via Algorithm and System Co-design. ICLR 2023 - [i6]Seyed Mahmoud Sajjadi Mohammadabadi, Syed Zawad, Feng Yan, Lei Yang:
Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach. CoRR abs/2312.05642 (2023) - 2022
- [c6]Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar
TIFF: Tokenized Incentive for Federated Learning. CLOUD 2022: 407-416 - [c5]Jingoo Han, Ahmad Faraz Khan, Syed Zawad, Ali Anwar
Heterogeneity-Aware Adaptive Federated Learning Scheduling. IEEE Big Data 2022: 911-920 - [p4]Syed Zawad, Feng Yan, Ali Anwar:
Introduction to Federated Learning Systems. Federated Learning 2022: 195-212 - [p3]Syed Zawad, Feng Yan, Ali Anwar:
Local Training and Scalability of Federated Learning Systems. Federated Learning 2022: 213-233 - [p2]Syed Zawad, Feng Yan, Ali Anwar:
Straggler Management. Federated Learning 2022: 235-258 - [p1]Syed Zawad, Feng Yan, Ali Anwar:
Systems Bias in Federated Learning. Federated Learning 2022: 259-278 - [i5]Ahsan Ali, Syed Zawad, Paarijaat Aditya, Istemi Ekin Akkus, Ruichuan Chen, Feng Yan:
SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training. CoRR abs/2205.01853 (2022) - [i4]Shreshth Tuli, Fatemeh Mirhakimi, Samodha Pallewatta, Syed Zawad, Giuliano Casale, Bahman Javadi, Feng Yan, Rajkumar Buyya, Nicholas R. Jennings:
AI Augmented Edge and Fog Computing: Trends and Challenges. CoRR abs/2208.00761 (2022) - 2021
- [c4]Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan:
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. AAAI 2021: 10807-10814 - [i3]Syed Zawad, Ahsan Ali
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. CoRR abs/2102.00655 (2021) - 2020
- [j1]Heyang Qin
Reinforcement-Learning-Empowered MLaaS Scheduling for Serving Intelligent Internet of Things. IEEE Internet Things J. 7(7): 6325-6337 (2020) - [c3]Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar
TiFL: A Tier-based Federated Learning System. HPDC 2020: 125-136 - [i2]Zheng Chai, Ahsan Ali
TiFL: A Tier-based Federated Learning System. CoRR abs/2001.09249 (2020)
2010 – 2019
- 2019
- [c2]Hsin-Pai Cheng, Patrick Yu, Haojing Hu, Syed Zawad, Feng Yan, Shiyu Li
Towards Decentralized Deep Learning with Differential Privacy. CLOUD 2019: 130-145 - [c1]Heyang Qin, Syed Zawad, Yanqi Zhou, Lei Yang, Dongfang Zhao, Feng Yan:
Swift machine learning model serving scheduling: a region based reinforcement learning approach. SC 2019: 13:1-13:23 - [i1]Yanqi Zhou, Peng Wang, Sercan Ömer Arik, Haonan Yu, Syed Zawad, Feng Yan, Greg Diamos:
EPNAS: Efficient Progressive Neural Architecture Search. CoRR abs/1907.04648 (2019)
Coauthor Index
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last updated on 2024-10-23 20:32 CEST by the dblp team
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