IEEE INFOCOM 2023
Federated Learning 1
Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning
Yunming Liao (University of Science and Technology of China, China); Yang Xu (University of Science and Technology of China & School of Computer Science and Technology, China); Hongli Xu and Lun Wang (University of Science and Technology of China, China); Chen Qian (University of California at Santa Cruz, USA)
Speaker
Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions
Fei Wang (Beijing University of Posts and Telecommunications, China); Lei Jiao (University of Oregon, USA); Konglin Zhu (Beijing University of Posts and Telecommunications, China); Xiaojun Lin (Purdue University, USA); Lei Li (Beijing University of Posts And Telecommunications, China)
Speaker
Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization
Heting Liu, Fang He and Guohong Cao (The Pennsylvania State University, USA)
Speaker
Asynchronous Federated Unlearning
Ningxin Su and Baochun Li (University of Toronto, Canada)
In this paper, we present the design and implementation of Knot, a new clustered aggregation mechanism custom-tailored to asynchronous federated learning. The design of Knot is based upon our intuition that client aggregation can be performed within each cluster only so that retraining due to data erasure can be limited to within each cluster as well. To optimize client-cluster assignment, we formulated a lexicographical minimization problem that could be transformed into a linear programming problem and solved efficiently. Over a variety of datasets and tasks, we have shown clear evidence that Knot outperformed the state-of-the-art federated unlearning mechanisms by up to 85% in the context of asynchronous federated learning.
Speaker Ningxin Su (University of Toronto)
Ningxin Su is a third-year Ph.D. student in the Department of Electrical and Computer Engineering, University of Toronto, under the supervision of Prof. Baochun Li. She received her M.E. and B.E. degrees from the University of Sheffield and Beijing University of Posts and Telecommunications in 2020 and 2019, respectively. Her research area includes distributed machine learning, federated learning and networking. Her website is located at ningxinsu.github.io.
Session Chair
Giovanni NEGLIA
Federated Learning 2
Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression
Zhida Jiang (University of Science and Technology of China, China); Yang Xu (University of Science and Technology of China & School of Computer Science and Technology, China); Hongli Xu and Zhiyuan Wang (University of Science and Technology of China, China); Chen Qian (University of California at Santa Cruz, USA)
Speaker
FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection
Xiong Wang and Yuxin Chen (Huazhong University of Science and Technology, China); Yuqing Li (Wuhan University, China); Xiaofei Liao and Hai Jin (Huazhong University of Science and Technology, China); Bo Li (Hong Kong University of Science and Technology, Hong Kong)
Speaker
Federated Learning with Flexible Control
Shiqiang Wang (IBM T. J. Watson Research Center, USA); Jake Perazzone (US Army Research Lab, USA); Mingyue Ji (University of Utah, USA); Kevin S Chan (US Army Research Laboratory, USA)
Speaker Shiqiang Wang (IBM T. J. Watson Research Center, USA)
Shiqiang Wang is a Staff Research Scientist at IBM T. J. Watson Research Center, NY, USA. He received his Ph.D. from Imperial College London, United Kingdom, in 2015. His current research focuses on the intersection of distributed computing, machine learning, networking, and optimization, with a broad range of applications including data analytics, edge-based artificial intelligence (Edge AI), Internet of Things (IoT), and future wireless systems. He received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize in 2021, IEEE ComSoc Best Young Professional Award in Industry in 2021, IBM Outstanding Technical Achievement Awards (OTAA) in 2019, 2021, and 2022, multiple Invention Achievement Awards from IBM since 2016, Best Paper Finalist of the IEEE International Conference on Image Processing (ICIP) 2019, and Best Student Paper Award of the Network and Information Sciences International Technology Alliance (NIS-ITA) in 2015.
Federated Learning under Heterogeneous and Correlated Client Availability
Angelo Rodio (Inria, France); Francescomaria Faticanti (INRIA, France); Othmane Marfoq (Inria, France & Accenture Technology Labs, France); Giovanni Neglia (Inria, France); Emilio Leonardi (Politecnico di Torino, Italy)
Our experimental results show that CA-Fed has higher time-average accuracy and a lower standard deviation than state-of-the-art AdaFed and F3AST.
Speaker
Session Chair
Sajal K. Das
Federated Learning 3
A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning
Yongheng Deng and Ju Ren (Tsinghua University, China); Cheng Tang and Feng Lyu (Central South University, China); Yang Liu and Yaoxue Zhang (Tsinghua University, China)
Speaker
Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis
Ming Tang (Southern University of Science and Technology, China); Vincent W.S. Wong (University of British Columbia, Canada)
Speaker
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Anran Li (Nanyang Technological University, Singapore); Hongyi Peng (Nanyang Technological University, Singapore & Alibaba Group, China); Lan Zhang and Jiahui Huang (University of Science and Technology of China, China); Qing Guo, Han Yu and Yang Liu (Nanyang Technological University, Singapore)
Speaker
Joint Participation Incentive and Network Pricing Design for Federated Learning
Ningning Ding (Northwestern University, USA); Lin Gao (Harbin Institute of Technology (Shenzhen), China); Jianwei Huang (The Chinese University of Hong Kong, Shenzhen, China)
Speaker
Session Chair
Danda B Rawat
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