Federated Learning
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Federated Learning
Theory and Practice
Edited by
Lam M. Nguyen
Trong Nghia Hoang
Pin-Yu Chen
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Contents
Contributors ........................................................................................ xvii
Preface.............................................................................................. xxiii
PART 1 Optimization fundamentals for secure
federated learning
CHAPTER 1 Gradient descent-type methods .................................... 3
Quoc Tran-Dinh and Marten van Dijk
1.1 Introduction...................................................................... 3
1.2 Basic components of GD-type methods .................................. 5
1.2.1 Search direction ....................................................... 6
1.2.2 Step-size ................................................................. 7
1.2.3 Proximal operator ..................................................... 8
1.2.4 Momentum ............................................................. 9
1.2.5 Dual averaging variant ............................................... 9
1.2.6 Structure assumptions............................................... 10
1.2.7 Optimality certification ............................................. 12
1.2.8 Unified convergence analysis ..................................... 13
1.2.9 Convergence rates and complexity analysis ................... 17
1.2.10 Initial point, warm-start, and restart ............................. 18
1.3 Stochastic gradient descent methods ..................................... 19
1.3.1 The algorithmic template........................................... 19
1.3.2 SGD estimators ....................................................... 20
1.3.3 Unified convergence analysis ..................................... 21
1.4 Concluding remarks .......................................................... 25
Acknowledgments ............................................................ 25
References ...................................................................... 25
CHAPTER 2 Considerations on the theory of training models
with differential privacy .............................................. 29
Marten van Dijk and Phuong Ha Nguyen
2.1 Introduction..................................................................... 29
2.2 Differential private SGD (DP-SGD) ...................................... 30
2.2.1 Clipping ................................................................ 31
2.2.2 Mini-batch SGD ...................................................... 32
2.2.3 Gaussian noise ........................................................ 33
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2.2.4 Aggregation at the server........................................... 34
2.2.5 Interrupt service routine ............................................ 35
2.2.6 DP principles and utility............................................ 35
2.2.7 Normalization ......................................................... 37
2.3 Differential privacy ........................................................... 38
2.3.1 Characteristics of a differential privacy measure ............. 39
2.3.2 (, δ)-differential privacy........................................... 40
2.3.3 Divergence-based DP measures .................................. 42
2.4 Gaussian differential privacy ............................................... 43
2.4.1 Gaussian DP ........................................................... 44
2.4.2 Subsampling........................................................... 44
2.4.3 Composition ........................................................... 45
2.4.4 Tight analysis of DP-SGD ......................................... 46
2.4.5 Strong adversarial model ........................................... 46
2.4.6 Group privacy ......................................................... 48
2.4.7 DP-SGD’s trade-off function ...................................... 49
2.5 Future work ..................................................................... 49
2.5.1 Using synthetic data ................................................. 49
2.5.2 Adaptive strategies ................................................... 50
2.5.3 DP proof: a weaker adversarial model .......................... 51
2.5.4 Computing environment with less adversarial
capabilities ............................................................. 51
References ...................................................................... 52
CHAPTER 3 Privacy-preserving federated learning: algorithms
and guarantees ............................................................ 57
Xinwei Zhang, Xiangyi Chen, Bingqing Song,
Prashant Khanduri, and Mingyi Hong
3.1 Introduction..................................................................... 57
3.2 Background and preliminaries ............................................. 58
3.2.1 The FedAvg algorithm .............................................. 58
3.2.2 Differential privacy .................................................. 59
3.3 DP guaranteed algorithms................................................... 61
3.3.1 Sample-level DP...................................................... 62
3.3.2 Client-level DP ....................................................... 64
3.4 Performance of clip-enabled DP-FedAvg ............................... 66
3.4.1 Main results ........................................................... 66
3.4.2 Experimental evaluation ............................................ 69
3.5 Conclusion and future work ................................................ 71
References ...................................................................... 72
Contents
CHAPTER 4 Assessing vulnerabilities and securing federated
learning ........................................................................ 75
Supriyo Chakraborty and Arjun Bhagoji
4.1 Introduction..................................................................... 75
4.2 Background and vulnerability analysis .................................. 75
4.2.1 Definitions and notation ............................................ 76
4.2.2 Vulnerability analysis ............................................... 77
4.3 Attacks on federated learning .............................................. 78
4.3.1 Training-time attacks ................................................ 79
4.3.2 Inference-time attacks............................................... 83
4.4 Defenses ......................................................................... 83
4.4.1 Protecting against training-time attacks ........................ 83
4.4.2 Protecting against inference-time attacks ...................... 86
4.5 Takeaways and future work ................................................. 86
References ...................................................................... 87
CHAPTER 5 Adversarial robustness in federated learning ............. 91
Chulin Xie and Xiaoyang Wang
5.1 Introduction..................................................................... 91
5.2 Attack in federated learning ................................................ 92
5.2.1 Targeted data poisoning attack .................................... 92
5.2.2 Untargeted model poisoning attack .............................. 96
5.3 Defense in federated learning .............................................. 97
5.3.1 Vector-wise defense ................................................. 98
5.3.2 Dimension-wise defense ........................................... 99
5.3.3 Certification ......................................................... 100
5.3.4 Personalization...................................................... 100
5.3.5 Differential privacy ................................................ 101
5.3.6 The gap between distributed training and federated
learning ............................................................... 101
5.3.7 Open problems and further work ............................... 102
5.4 Conclusion .................................................................... 102
References .................................................................... 103
CHAPTER 6 Evaluating gradient inversion attacks and
defenses..................................................................... 105
Yangsibo Huang, Samyak Gupta, Zhao Song,
Sanjeev Arora, and Kai Li
6.1 Introduction................................................................... 105
6.2 Gradient inversion attacks................................................. 106
6.3 Strong assumptions made by SOTA attacks .......................... 107
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6.3.1 The state-of-the-art attacks ...................................... 107
6.3.2 Strong assumptions ................................................ 107
6.3.3 Re-evaluation under relaxed assumptions .................... 109
6.4 Defenses against the gradient inversion attack ....................... 110
6.4.1 Encrypt gradients................................................... 110
6.4.2 Perturbing gradients ............................................... 111
6.4.3 Weak encryption of inputs (encoding inputs) ............... 111
6.5 Evaluation ..................................................................... 112
6.5.1 Experimental setup ................................................ 112
6.5.2 Performance of defense methods ............................... 114
6.5.3 Performance of combined defenses............................ 116
6.5.4 Time estimate for end-to-end recovery of a single
image .................................................................. 116
6.6 Conclusion .................................................................... 117
6.7 Future directions............................................................. 118
6.7.1 Gradient inversion attacks for text data ....................... 118
6.7.2 Gradient inversion attacks in variants of federated
learning ............................................................... 118
6.7.3 Defenses with provable guarantee ............................. 119
References .................................................................... 119
PART 2 Emerging topics
CHAPTER 7 Personalized federated learning: theory and open
problems .................................................................... 125
Canh T. Dinh, Tung T. Vu, and Nguyen H. Tran
7.1 Introduction................................................................... 125
7.2 Problem formulation of pFL.............................................. 126
7.3 Review of personalized FL approaches ................................ 128
7.3.1 Mixing models ...................................................... 128
7.3.2 Model-based approaches: meta-learning ..................... 129
7.3.3 Multi-task learning................................................. 130
7.3.4 Weight sharing ...................................................... 131
7.3.5 Clients clustering................................................... 131
7.4 Personalized FL algorithms............................................... 132
7.4.1 pFedMe ............................................................... 132
7.4.2 FedU .................................................................. 134
7.5 Experiments .................................................................. 135
7.5.1 Experimental settings ............................................. 135
7.5.2 Comparison.......................................................... 136
7.6 Open problems ............................................................... 137
Contents
7.6.1 Transfer learning ................................................... 137
7.6.2 Knowledge distillation ............................................ 138
7.7 Conclusion .................................................................... 138
References .................................................................... 138
CHAPTER 8 Fairness in federated learning .................................. 143
Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu,
Rachael Hwee Ling Sim, See-Kiong Ng,
Chuan-Sheng Foo, Patrick Jaillet, Trong Nghia Hoang,
and Bryan Kian Hsiang Low
8.1 Introduction................................................................... 143
8.2 Notions of fairness .......................................................... 144
8.2.1 Equitable fairness .................................................. 144
8.2.2 Collaborative fairness ............................................. 148
8.2.3 Algorithmic fairness ............................................... 151
8.3 Algorithms to achieve fairness in FL ................................... 153
8.3.1 Algorithms to achieve equitable fairness ..................... 153
8.3.2 Algorithms to achieve collaborative fairness ................ 155
8.3.3 Algorithms to achieve algorithmic fairness .................. 157
8.4 Open problems and conclusion .......................................... 157
Acknowledgments .......................................................... 158
References .................................................................... 158
CHAPTER 9 Meta-federated learning ............................................ 161
Omid Aramoon, Pin-Yu Chen, Gang Qu, and Yuan Tian
9.1 Introduction................................................................... 161
9.2 Background ................................................................... 164
9.2.1 Federated learning ................................................. 164
9.2.2 Secure aggregation................................................. 164
9.2.3 Robust aggregation rules and defenses........................ 165
9.3 Problem definition and threat model.................................... 166
9.4 Meta-federated learning ................................................... 167
9.5 Experimental evaluation and discussion ............................... 169
9.5.1 Datasets and experiment setup .................................. 169
9.5.2 Utility of meta-FL.................................................. 171
9.5.3 Robustness of meta-FL ........................................... 171
9.6 Conclusion .................................................................... 177
References .................................................................... 177
CHAPTER 10 Graph-aware federated learning ................................ 181
Songtao Lu, Pengwei Xing, and Han Yu
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10.1 Introduction................................................................... 181
10.2 Decentralized federated learning ........................................ 182
10.3 Multi-center federated learning .......................................... 186
10.4 Graph-knowledge based federated learning........................... 188
10.4.1 Applications of BiG-FL .......................................... 188
10.4.2 Algorithm design for BiG-FL ................................... 190
10.5 Numerical evaluation of GFL models .................................. 192
10.5.1 Results on synthetic data ......................................... 192
10.5.2 Results on real-world data for NLP............................ 193
10.6 Summary ...................................................................... 195
References .................................................................... 196
CHAPTER 11 Vertical asynchronous federated learning:
algorithms and theoretic guarantees ........................ 199
Tianyi Chen, Xiao Jin, Yuejiao Sun, and Wotao Yin
11.1 Introduction................................................................... 199
11.1.1 This chapter ......................................................... 200
11.1.2 Related work ........................................................ 200
11.2 Vertical federated learning ................................................ 201
11.2.1 Problem statement ................................................. 201
11.2.2 Asynchronous client updates .................................... 203
11.2.3 Types of flexible update rules ................................... 205
11.3 Convergence analysis....................................................... 205
11.3.1 Convergence under bounded delay ............................ 206
11.3.2 Convergence under stochastic unbounded delay ........... 208
11.4 Perturbed local embedding for smoothness ........................... 208
11.4.1 Local perturbation ................................................. 208
11.4.2 Enforcing smoothness............................................. 209
11.5 Numerical tests .............................................................. 210
11.5.1 VAFL for federated logistic regression ....................... 210
11.5.2 VAFL for federated deep learning ............................. 211
Acknowledgments .......................................................... 214
References .................................................................... 214
CHAPTER 12 Hyperparameter tuning for federated learning –
systems and practices ............................................... 219
Syed Zawad and Feng Yan
12.1 Introduction................................................................... 219
12.2 Systems resources ........................................................... 221
12.3 Cross-device FL hyperparameters....................................... 222
Contents
12.3.1 Client-side hyperparameters ..................................... 222
12.3.2 Server-side hyperparameters .................................... 223
12.4 System challenges in FL HPO ........................................... 224
12.4.1 Data privacy ......................................................... 225
12.4.2 Data heterogeneity ................................................. 225
12.4.3 Resource limitations............................................... 225
12.4.4 Scalability............................................................ 226
12.4.5 Resource heterogeneity ........................................... 227
12.4.6 Dynamic data ....................................................... 228
12.4.7 Participation fairness and client dropouts .................... 228
12.5 State-of-the-art ............................................................... 229
12.6 Conclusion .................................................................... 232
References .................................................................... 232
CHAPTER 13 Hyper-parameter optimization in federated
learning ...................................................................... 237
Yi Zhou, Parikshit Ram, Theodoros Salonidis,
Nathalie Baracaldo, Horst Samulowitz, and Heiko Ludwig
13.1 Introduction................................................................... 237
13.1.1 FL-HPO problem definition ..................................... 237
13.1.2 Challenges and goals .............................................. 239
13.2 State-of-the-art FL-HPO approaches ................................... 240
13.3 FLoRA: a single-shot FL-HPO approach ............................. 241
13.3.1 The main algorithm: leveraging local HPOs................. 242
13.3.2 Loss surface aggregation ......................................... 244
13.3.3 Optimality guarantees for FLoRA ............................. 245
13.4 Empirical evaluation........................................................ 247
13.5 Conclusion .................................................................... 252
References .................................................................... 252
CHAPTER 14 Federated sequential decision making: Bayesian
optimization, reinforcement learning, and beyond ... 257
Zhongxiang Dai, Flint Xiaofeng Fan, Cheston Tan,
Trong Nghia Hoang, Bryan Kian Hsiang Low, and
Patrick Jaillet
14.1 Introduction................................................................... 257
14.2 Federated Bayesian optimization ........................................ 259
14.2.1 Background on Bayesian optimization ....................... 259
14.2.2 Background on federated Bayesian optimization .......... 260
14.2.3 Overview of representative existing works on FBO ....... 261
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14.2.4 Algorithms for FBO ............................................... 262
14.2.5 Theoretical and empirical results for FBO ................... 264
14.3 Federated reinforcement learning ....................................... 265
14.3.1 Background on reinforcement learning ....................... 265
14.3.2 Background on federated reinforcement learning .......... 266
14.3.3 Overview of representative existing works on FRL........ 268
14.3.4 Frameworks and algorithms for FRL.......................... 269
14.3.5 Theoretical and empirical results for FRL ................... 270
14.4 Related work ................................................................. 272
14.4.1 Federated Bayesian optimization............................... 272
14.4.2 Federated reinforcement learning .............................. 273
14.4.3 Federated bandits................................................... 273
14.5 Open problems and future directions ................................... 274
Acknowledgments .......................................................... 275
References .................................................................... 275
CHAPTER 15 Data valuation in federated learning ......................... 281
Zhaoxuan Wu, Xinyi Xu, Rachael Hwee Ling Sim,
Yao Shu, Xiaoqiang Lin, Lucas Agussurja,
Zhongxiang Dai, See-Kiong Ng, Chuan-Sheng Foo,
Patrick Jaillet, Trong Nghia Hoang, and
Bryan Kian Hsiang Low
15.1 Introduction................................................................... 281
15.2 Data valuation: motivations and incentives ........................... 282
15.3 Simple valuation methods ................................................. 282
15.4 Related work: conventional data valuation ............................ 284
15.4.1 Utility functions .................................................... 284
15.4.2 Valuation functions ................................................ 287
15.5 Extending to the federated setting: does it work? ................... 288
15.6 Vertical data valuation: feature valuation .............................. 290
15.6.1 Feature importance and attribution ............................ 290
15.6.2 Information-driven valuation .................................... 290
15.7 Horizontal data valuation: gradient valuation ........................ 290
15.7.1 Gradient contributions ............................................ 291
15.7.2 Similarity-driven gradient valuation ........................... 292
15.8 Learning-based valuation.................................................. 293
15.9 Conclusion and future work .............................................. 294
Acknowledgments .......................................................... 295
References .................................................................... 295
Contents
PART 3 Applications & ethical considerations
CHAPTER 16 Incentives in federated learning ............................... 299
Rachael Hwee Ling Sim, Sebastian Shenghong Tay,
Xinyi Xu, Yehong Zhang, Zhaoxuan Wu, Xiaoqiang Lin,
See-Kiong Ng, Chuan-Sheng Foo, Patrick Jaillet,
Trong Nghia Hoang, and Bryan Kian Hsiang Low
16.1 Overview and motivation .................................................. 299
16.2 Problem setting .............................................................. 300
16.3 Incentives...................................................................... 300
16.4 Contribution evaluation .................................................... 301
16.5 Client selection .............................................................. 302
16.6 Reward allocation ........................................................... 303
16.7 Other incentives ............................................................. 304
16.8 Monetary rewards ........................................................... 305
16.9 Non-monetary rewards ..................................................... 306
16.9.1 Non-FL setting...................................................... 307
16.9.2 FL setting ............................................................ 308
16.10 Conclusion and future work .............................................. 308
Acknowledgments .......................................................... 308
References .................................................................... 309
CHAPTER 17 Introduction to quantum federated machine
learning ...................................................................... 311
Samuel Yen-Chi Chen and Shinjae Yoo
17.1 Introduction................................................................... 311
17.2 Quantum federated learning .............................................. 312
17.3 Variational quantum circuits .............................................. 314
17.3.1 Quantum encoder .................................................. 316
17.3.2 Quantum gradients ................................................. 317
17.4 Demonstration ............................................................... 318
17.5 Advanced settings ........................................................... 321
17.6 Discussion..................................................................... 322
17.6.1 Integration with other privacy-preserving mechanisms ... 322
17.6.2 Various aggregation methods.................................... 322
17.7 Conclusion .................................................................... 323
References .................................................................... 323
CHAPTER 18 Federated quantum natural gradient descent for
quantum federated learning ...................................... 329
Jun Qi and Min-Hsiu Hsieh
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18.1 Introduction................................................................... 329
18.2 Variational quantum circuit ............................................... 331
18.3 Quantum natural gradient descent....................................... 333
18.4 Quantum natural gradient descent for VQC .......................... 334
18.5 Federated quantum natural gradient descent.......................... 335
18.6 Experimental results ........................................................ 337
18.7 Conclusion and discussion ................................................ 339
References .................................................................... 339
CHAPTER 19 Mobile computing framework for federated
learning ...................................................................... 343
Xiang Chen, Fuxun Yu, and Zirui Xu
19.1 Federated learning on mobile platforms ............................... 343
19.2 Challenge in mobile-based federated learning ....................... 343
19.3 Helios: a self-coordinated federated learning framework for
mobile platform.............................................................. 345
19.3.1 Framework overview .............................................. 345
19.3.2 Straggler identification in heterogeneous collaboration .. 346
19.3.3 Optimization target determination ............................. 347
19.3.4 Soft-training on straggling mobile devices................... 347
19.4 Performance evaluation .................................................... 349
19.4.1 General helios performance evaluation ....................... 350
19.4.2 Model aggregation optimization evaluation ................. 351
19.5 Conclusion and future directions ........................................ 351
References .................................................................... 352
CHAPTER 20 Federated learning for privacy-preserving speech
recognition ................................................................. 353
Chao-Han Huck Yang and Sabato Marco Siniscalchi
20.1 From voice protection to federated assistant.......................... 353
20.1.1 Introduction of automatic speech recognition ............... 353
20.1.2 New data regulation and federated speech processing .... 354
20.1.3 Distributed training and federated approaches .............. 355
20.1.4 Differential privacy and federated speech assistant ........ 357
20.1.5 Teacher-student learning for federated ASR................. 358
20.2 Federated speech recognition with synthetic data ................... 360
20.3 Conclusion .................................................................... 364
References .................................................................... 364
Contents
CHAPTER 21 Ethical considerations and legal issues relating to
federated learning ..................................................... 369
Warren Chik and Florian Gamper
21.1 Introduction................................................................... 369
21.2 Global trends and ethical guidelines for trustworthy AI and the
universal fundamental principles ........................................ 370
21.2.1 Principles for ethical data sharing .............................. 370
21.2.2 Principles for ethical AI/ML .................................... 372
21.3 Privacy and personal data rights and the international data
protection regime ............................................................ 376
21.3.1 Does federated learning involve the processing of data? . 376
21.3.2 Other issues in relation to the GDPR .......................... 379
21.4 Intellectual property rights relating to federated learning
systems......................................................................... 381
21.4.1 How to protect models created by federated learning ..... 381
21.4.2 Avoiding IP infringement ........................................ 383
21.5 Governance structure ....................................................... 385
21.5.1 Common contractual federated learning projects .......... 386
21.6 Conclusion .................................................................... 388
Acknowledgments .......................................................... 388
References .................................................................... 389
Index ................................................................................................. 393
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Contributors
Lucas Agussurja
National University of Singapore, Singapore, Singapore
Omid Aramoon
University of Maryland at College Park, College Park, MD, United States
Sanjeev Arora
Princeton University, Princeton, NJ, United States
Nathalie Baracaldo
IBM Research, Yorktown Heights, NY, United States
Arjun Bhagoji
Department of Computer Science, University of Chicago, Chicago, IL,
United States
Supriyo Chakraborty
Distributed AI, IBM Research, Yorktown Heights, NY, United States
Pin-Yu Chen
IBM Research, Yorktown Heights, NY, United States
Samuel Yen-Chi Chen
Brookhaven National Laboratory, Upton, NY, United States
Tianyi Chen
Rensselaer Polytechnic Institute, Troy, NY, United States
Xiang Chen
George Mason University, ECE, Fairfax, VA, United States
Xiangyi Chen
University of Minnesota, Minneapolis, MN, United States
Warren Chik
Singapore Management University, Singapore, Singapore
Zhongxiang Dai
National University of Singapore, Singapore, Singapore
Canh T. Dinh
The University of Sydney, Darlington, NSW, Australia
Flint Xiaofeng Fan
National University of Singapore, Singapore, Singapore
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Contributors
Chuan-Sheng Foo
National University of Singapore, Singapore, Singapore
Agency for Science, Technology and Research, Singapore, Singapore
Florian Gamper
Singapore Management University, Singapore, Singapore
Samyak Gupta
Princeton University, Princeton, NJ, United States
Trong Nghia Hoang
Washington State University, Pullman, WA, United States
Mingyi Hong
University of Minnesota, Minneapolis, MN, United States
Min-Hsiu Hsieh
Hon Hai (Foxconn) Quantum Computing Research Center, Taipei, Taiwan
Yangsibo Huang
Electrical and Computer Engineering, Princeton University, Princeton, NJ,
United States
Patrick Jaillet
Massachusetts Institute of Technology, Cambridge, MA, United States
Xiao Jin
Rensselaer Polytechnic Institute, Troy, NY, United States
Prashant Khanduri
University of Minnesota, Minneapolis, MN, United States
Kai Li
Princeton University, Princeton, NJ, United States
Xiaoqiang Lin
National University of Singapore, Singapore, Singapore
Bryan Kian Hsiang Low
National University of Singapore, Singapore, Singapore
Songtao Lu
IBM Thomas J. Watson Research Center, Yorktown Heights, NY, United States
Heiko Ludwig
IBM Research, Yorktown Heights, NY, United States
See-Kiong Ng
National University of Singapore, Singapore, Singapore
Contributors
Phuong Ha Nguyen
eBay Inc., San Jose, CA, United States
Jun Qi
Fudan University, Shanghai, China
Gang Qu
University of Maryland at College Park, College Park, MD, United States
Parikshit Ram
IBM Research, Yorktown Heights, NY, United States
Theodoros Salonidis
IBM Research, Yorktown Heights, NY, United States
Horst Samulowitz
IBM Research, Yorktown Heights, NY, United States
Yao Shu
National University of Singapore, Singapore, Singapore
Rachael Hwee Ling Sim
National University of Singapore, Singapore, Singapore
Sabato Marco Siniscalchi
University of Enna, Enna, Italy
Norwegian University of Science and Technology, Trondheim, Norway
Bingqing Song
University of Minnesota, Minneapolis, MN, United States
Zhao Song
Adobe Research, San Jose, CA, United States
Yuejiao Sun
University of California, Los Angeles, CA, United States
Cheston Tan
Institute for Infocomm Research, A*STAR, Singapore, Singapore
Sebastian Shenghong Tay
National University of Singapore, Singapore, Singapore
Yuan Tian
University of Virginia, Charlottesville, VA, United States
Nguyen H. Tran
The University of Sydney, Darlington, NSW, Australia
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Contributors
Quoc Tran-Dinh
Department of Statistics and Operations Research, The University of North
Carolina at Chapel Hill, Chapel Hill, NC, United States
Marten van Dijk
CWI, Amsterdam, Netherlands
VU University Amsterdam, Amsterdam, Netherlands
Tung T. Vu
Queen’s University Belfast, Belfast, United Kingdom
Xiaoyang Wang
University of Illinois at Urbana-Champaign, Urbana, IL, United States
Zhaoxuan Wu
National University of Singapore, Singapore, Singapore
Chulin Xie
University of Illinois at Urbana-Champaign, Urbana, IL, United States
Pengwei Xing
Nanyang Technological University, Singapore, Singapore
Xinyi Xu
National University of Singapore, Singapore, Singapore
Zirui Xu
CVS Health, Richmond, VA, United States
Feng Yan
University of Houston, Houston, TX, United States
Chao-Han Huck Yang
Nvidia Research, Taipei, Taiwan
Wotao Yin
Alibaba Group, Bellevue, WA, United States
Shinjae Yoo
Brookhaven National Laboratory, Upton, NY, United States
Fuxun Yu
Microsoft, Seattle, WA, United States
Han Yu
Nanyang Technological University, Singapore, Singapore
Syed Zawad
University of Nevada, Reno, NV, United States
Contributors
Xinwei Zhang
University of Minnesota, Minneapolis, MN, United States
Yehong Zhang
Peng Cheng Laboratory, Shenzhen, People’s Republic of China
Yi Zhou
IBM Research, Yorktown Heights, NY, United States
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