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Snowball Effect in Federated Learning: An Approach of Exponentially Expanding Structures for Optimizing the Training Efficiency
Guoliang Cheng;Peichun Li;Beihai Tan;Rong Yu;Yuan Wu;Miao Pan
IEEE Transactions on Cognitive Communications and Networking
Year: 2024 | Early Access Article |
Efficient federated learning (FL) in mobile edge networks faces challenges due to energy-consuming on-device training and wireless transmission. Optimizing the neural network structures is an effective approach to achieving energy savings. In this paper, we present a Snowball FL training with expanding neural network structure, which starts with a small-sized submodel and gradually progresses to a...Show More
Multi-Dimensional Contract Design for Energy-Efficient Wireless Sensing in Consumer-Centric E-Commerce Systems
E-commerce systems employ Consumer Internet of Things (CIoT) devices, like voice assistants, wearables, and smart plugs, to sense and collect massive data on commodities and consumer behavior. This data is then used to analyze e-commerce patterns, predict consumer behavior, and provide recommendations for consumer-centric e-commerce services. However, this leads to high energy consumption for the ...Show More
Federated Split Learning With Data and Label Privacy Preservation in Vehicular Networks
Maoqiang Wu;Guoliang Cheng;Dongdong Ye;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan
Federatedlearning (FL) is an emerging distributed learning paradigm widely used in vehicular networks, where vehicles are enabled to train the deep model for the server while keeping private data locally. However, the annotation of private data by vehicular users is very difficult since the high costs and professional needs, and one solution is that roadside infrastructures could provide label map...Show More
Split Learning with Differential Privacy for Integrated Terrestrial and Non-Terrestrial Networks
Maoqiang Wu;Guoliang Cheng;Peichun Li;Rong Yu;Yuan Wu;Miao Pan;Rongxing Lu
IEEE Wireless Communications
Cited by: Papers (17)
Integrated terrestrial and non-terrestrial networks (TNTNs) have become a promising architecture for enabling ubiquitous connectivity. Smart remote sensing is one of the typical applications of TNTNs that collects and analyzes various dimensions of remote sensing data by deploying Internet of Things (IoT) sensors and edge computing in terrestrial, space, aerial, and underwater networks. To improve...Show More
Resting Your Car for Cash: Incentive Mechanism Based TNC Car Scheduling for Carbon Neutrality
Maoqiang Wu;Guoliang Cheng;Pavana Prakash;Rong Yu;Xiaoming Xiong;Miao Pan
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2023 | Conference Paper |
The transportation network company (TNC) services (i.e., Uber, Lyft, Didi, etc.) can effectively match the vehicles/drivers with passengers via mobile applications, and solve the first- and last-mile problem. However, most idle TNC vehicles have to travel around looking for the next order, which causes unnecessary carbon emissions. In addition, the idle TNC vehicles tend to move to the higher-trip...Show More
FAST: Fidelity-Adjustable Semantic Transmission Over Heterogeneous Wireless Networks
Peichun Li;Guoliang Cheng;Jiawen Kang;Rong Yu;Liping Qian;Yuan Wu;Dusit Niyato
ICC 2023 - IEEE International Conference on Communications
Year: 2023 | Conference Paper |
Cited by: Papers (7)
In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub-model training scheme to learn the flex...Show More
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
Peichun Li;Guoliang Cheng;Xumin Huang;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan
IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
Year: 2023 | Conference Paper |
Cited by: Papers (14)
In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model ...Show More
Snowball: Energy Efficient and Accurate Federated Learning With Coarse-to-Fine Compression Over Heterogeneous Wireless Edge Devices
Peichun Li;Guoliang Cheng;Xumin Huang;Jiawen Kang;Rong Yu;Yuan Wu;Miao Pan;Dusit Niyato
Model update compression is a widely used technique to alleviate the communication cost in federated learning (FL). However, there is evidence indicating that the compression-based FL system often suffers the following two issues, i) the implicit learning performance deterioration of the global model due to the inaccurate update, ii) the limitation of sharing the same compression rate over heterog...Show More
Online Course Construction and Multi-dimensional Mixed Teaching: Research and Practice
2021 16th International Conference on Computer Science & Education (ICCSE)
Year: 2021 | Conference Paper |
Online course is a promising education paradigm to offer easy-to-access knowledge for everyone. However, the overemphasis on online education may lead to the fragmentation of knowledge and distract attention away from the on-campus courses for the student. To tackle this dilemma, we investigate the Flipped Classroom teaching model by combing the advantages of online and on-campus courses in this p...Show More
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