IEEE INFOCOM 2023
Matching DNN Compression and Cooperative Training with Resources and Data Availability
Francesco Malandrino (CNR-IEIIT, Italy); Giuseppe Di Giacomo (Politecnico Di Torino, Italy); Armin Karamzade (University of California Irvine, USA); Marco Levorato (University of California, Irvine, USA); Carla Fabiana Chiasserini (Politecnico di Torino & CNIT, IEIIT-CNR, Italy)
Speaker Carla Fabiana Chiasserini (Politecnico di Torino)
Carla Fabiana Chiasserini is an is an IEEE Fellow and a Full Professor at Politecnico di Torino, Italy. Her research interests include architectures, protocols, and performance analysis of wireless networks and networking support to machine learning.
On the Limit Performance of Floating Gossip
Gianluca Rizzo (HES SO Valais, Switzerland & Universita' di Foggia, Italy); Noelia Perez Palma (University of Murcia & University Carlos III, Spain); Vincenzo Mancuso and Marco G Ajmone Marsan (IMDEA Networks Institute, Spain)
We consider dynamic scenarios where continuous learning is required, and we adopt a mean field approach to investigate the limit performance of FG in terms of amount of data that users can incorporate into their models, as a function of the main system parameters. Differently from existing approaches in which either communication or computing aspects of GL are analyzed and optimized, our approach accounts for the compound impact of both aspects. We validate our results through detailed simulations, proving good accuracy. Our model shows that Floating Gossip can be very effective in implementing continuous training and update of machine learning models in a cooperative manner, and based on opportunistic exchanges among moving users.
Speaker Gianluca Rizzo
Gianluca Rizzo received the degree in Electronic Engineering from Politecnico di Torino, Italy, in 2001. From September 2001 to December 2003, he has been a researcher in Telecom Italia Lab, Torino, Italy. From 2004 to 2008 he has been at EPFL Lausanne, where in 2008 he received his PhD in Computer Science. From 2009 to 2013 he has been Staff Researcher at Institute IMDEA Networks in Madrid, Spain. Since April 2013 he is Senior Researcher at HES SO Valais, Switzerland. His research interests are in performance evaluation of Computer Networks, and particularly on Network Calculus, and in Green Networking.
Communication-Aware DNN Pruning
Tong Jian, Debashri Roy, Batool Salehihikouei, Nasim Soltani, Kaushik Chowdhury and Stratis Ioannidis (Northeastern University, USA)
Speaker Tong Jian (Analog Devices; Northeastern University)
Tong Jian is a Machine Learning Scientist at Analog Devices, in Boston, MA, where she is working on AI for Science and building AI solutions for intelligent edge. She completed her Ph.D. in Computer Engineering from Northeastern University in Boston 2022, where she specialized in researching adversarial robustness and applied machine learning for wireless communication. During her Ph.D., she gained industry experience through internships at Nokia Bell Labs, where she worked on indoor WiFi localization, and at Amazon, focusing on improving their SOTA recommendation systems.
OPA: One-Predict-All For Efficient Deployment
Junpeng Guo, Shengqing Xia and Chunyi Peng (Purdue University, USA)
Speaker Junpeng Guo (Purdue University)
Junpeng Guo is a Ph.D. candidate at Purdue University supervised by Prof.Chunyi Peng. His research interests are in the interdisciplinary field of mobile computing and computer vision, with a focus on building efficient mobile vision systems. He is currently seeking a summer internship in either a research lab or industry in the upcoming seasons.
Christopher G. Brinton
Network Design and Fault Tolerance
Distributed Demand-aware Network Design using Bounded Square Root of Graphs
Or Peres (Ben Gurion University, Israel); Chen Avin (Ben-Gurion University of the Negev, Israel)
This paper draws a connection between the k-root of graphs and the network design problem and uses forests-decomposition of the demand as the primary methodology. In turn, we provide new algorithms for demand-aware network design, including cases where our algorithms are (order) optimal and improve previous results. In addition, we provide, for the first time and for the case of bounded arboricity, i) an efficient distributed algorithm for the CONGEST model and ii) an efficient and PRAM-based parallel algorithm. We also present empirical results on real-world demand matrices where our algorithms produce both low-degree, and low expected path length network designs.
Speaker Chen Avin
Chen Avin is a Professor at the School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Israel. He received his MSc and Ph.D. in computer science from the University of California, Los Angeles (UCLA) in 2003 and 2006. Recently he served as the chair of the Communication Systems Engineering department at BGU. His current research interests are data-driven graphs and network algorithms, modeling, and analysis, emphasizing demand-aware networks, distributed systems, social networks, and randomized algorithms for networking.
A Fast and Exact Evaluation Algorithm for the Expected Number of Connected Nodes: an Enhanced Network Reliability Measure
Kengo Nakamura (NTT Corporation & Kyoto University, Japan); Takeru Inoue (NTT Network Innovation Labs., Japan); Masaaki Nishino and Norihito Yasuda (NTT Comunication Science Laboratories, Japan); Shin-ichi Minato (Kyoto University, Japan)
This paper proposes an efficient method that exactly computes ECP. Our method performs dynamic programming just once without explicit repetition for each node pair and obtains an exact ECP value weighted by the number of users at each node. A thorough complexity analysis reveals that our method is faster than an existing reliability evaluation method, which can be transferred to ECP computation, by \(O(n)\). Numerical experiments using real topologies show great efficiency; e.g., our method computes the ECP of an 821-link network in ten seconds; the existing method cannot complete it in an hour. This paper also presents two applications: critical link identification and optimal resource (e.g., a server) placement.
Speaker Kengo Nakamura (NTT Corporation & Kyoto University)
Kengo Nakamura received the B.E. and M.E. degrees in information science and technology from the University of Tokyo, Japan, in 2016 and 2018. I am currently working as a researcher at NTT Communication Science Laboratories, Japan, and pursuing the Ph.D. degree from Kyoto University, Japan.
Network Slicing: Market Mechanism and Competitive Equilibria
Panagiotis Promponas and Leandros Tassiulas (Yale University, USA)
Speaker Panagiotis Promponas
Panagiotis Promponas (Graduate Student Member, IEEE) received the Diploma degree in electrical and computer engineering (ECE) from the National Technical University of Athens (NTUA), Greece, in 2019. He is currently a PhD student in the Electrical Engineering department at Yale University. Primarily, his research interests center around the field of resource allocation in constrained interdependent systems, with particular applications in the areas of quantum networks and wireless networks. He was a recipient of the Best Paper Award at the 12th IFIP WMNC 2019.
Tomography-based Progressive Network Recovery and Critical Service Restoration after Massive Failures
Viviana Arrigoni, Matteo Prata and Novella Bartolini (Sapienza University of Rome, Italy)
Speaker Viviana Arrigoni (Sapienza University of Rome)
Viviana is a research fellow at the Department of Computer Science at Sapienza, University of Rome. She got her PhD from the same department and authored several research papers. Her research interests are networking, network monitoring and computational linear algebra.
Memory/Cache Management 2
Two-level Graph Caching for Expediting Distributed GNN Training
Zhe Zhang, Ziyue Luo and Chuan Wu (The University of Hong Kong, Hong Kong)
Speaker Zhe Zhang (The University of Hong Kong)
Zhe Zhang is currently a Ph.D. candidate in the Department of Computer Science, The University of Hong Kong. She received her B.E. degree in 2019, from the Department of Computer Science and Technology, Zhejiang University. Her research interests include distributed machine learning algorithms and systems.
Galliot: Path Merging Based Betweenness Centrality Algorithm on GPU
Zheng Zhigao and Bo Du (Wuhan University, China)
Speaker Xie Peichen
Xie Peichen is a postgraduate at Wuhan University and an undergraduate student at Xiamen University, focuses on high-performance computing and graph computing. He has published papers as co-author in INFOCOM 2023 and JSAC and both were accepted. He won the highest award of Outstanding Winner in the Mathematical Contest in Modeling in 2021.
Economic Analysis of Joint Mobile Edge Caching and Peer Content Sharing
Changkun Jiang (Shenzhen University, China)
Speaker Changkun Jiang (Shenzhen University, China)
Changkun Jiang received his Ph.D. in Information Engineering from The Chinese University of Hong Kong in 2017. He is currently a faculty member in the College of Computer Science and Software Engineering at Shenzhen University, China. His research interests are primarily in artificial intelligence and economics for networked systems.
Enabling Switch Memory Management for Distributed Training with In-Network Aggregation
Bohan Zhao (Tsinghua University, China); Jianbo Dong and Zheng Cao (Alibaba Group, China); Wei Nie (Shenzhen University, unknown); Chang Liu (Shenzhen University, China); Wenfei Wu (Peking University, China)
Speaker Bohan Zhao (Tsinghua University)
Bohan Zhao is a Ph.D. candidate at Tsinghua University. His research interests include programmable networks and the information infrastructure for distributed applications, such as machine learning, high-performance computing, and big data.
Federated Learning 6
A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning
Ruiting Zhou (Southeast University, China); Jieling Yu and Ruobei Wang (Wuhan University, China); Bo Li (Hong Kong University of Science and Technology, Hong Kong); Jiacheng Jiang and Libing Wu (Wuhan University, China)
Speaker Jieling Yu（Wuhan University）
Jieling Yu received the BE degree from the School of Cyber Science and Engineering, Wuhan University, China, in 2021. She is currently working toward the master’s degree from the School of Cyber Science and Engineering, Wuhan University, China. Her research interests include edge computing, federated learning, online learning and network optimization.
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
Peichun Li (Guangdong University of Technology, China & University of Macau, Macao); Guoliang Cheng and Xumin Huang (Guangdong University of Technology, China); Jiawen Kang (Nanyang Technological University, Singapore); Rong Yu (Guangdong University of Technology, China); Yuan Wu (University of Macau, Macao); Miao Pan (University of Houston, USA)
By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.
Speaker Peichun Li (University of Macau)
Peichun Li received his M.S. degree from the Guangdong University of Technology. He is currently a research assistant at the University of Macau. His research interests include edge computing and deep learning, particularly in efficient algorithms for artificial intelligence applications.
AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation
Haozhao Wang (Huazhong University of Science and Technology, China); Wenchao Xu and Yunfeng Fan (The Hong Kong Polytechnic University, China); Ruixuan Li (Huazhong University of Science and Technology, China); Pan Zhou (School of CSE, Huazhong University of Science and Technology, China)
Speaker Haozhao Wang
Haozhao Wang is currently doing postdoctoral research in the School of Computer Science and Technology at Huazhong University of Science and Technology. He obtained his Ph.D. from the same university in 2021 and obtained his bachelor's degree from the University of Electronic Science and Technology. He was a research assistant in the Department of Computing at The Hong Kong Polytechnic University. His research interests include Edge Learning and Federated Learning.
Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated Learning
Tao Wu (National University of Defense Technology, China); Yuben Qu (Nanjing University of Aeronautics and Astronautics, China); Chunsheng Liu (National University of Defense Technology, China); Yuqian Jing (Nanjing University Of Aeronautics And Astronautics, China); Feiyu Wu (Nanjing University of Aeronautics and Astronautics, China); Haipeng Dai (Nanjing University & State Key Laboratory for Novel Software Technology, China); Chao Dong (Nanjing University of Aeronautics and Astronautics, China); Jiannong Cao (Hong Kong Polytechnic Univ, Hong Kong)
Speaker Tao Wu (National University of Defense Technology, China