Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach
By Wei Yang Bryan Lim, Jianqiang Huang, Zehui Xiong, Jiawen Kang, Dusist Niyato, Xiansheng Hua, Cyril Leung, Chunyan Miao. 2020
273 citations recorded by the source corpus.
Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is becoming increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owners, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing, computation, and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design, and shows the efficiency of our matching, thus guaranteeing profit maximization for the model owner amid information asymmetry.
Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Peilin Zhao
- Ming Ding
- Ramesh Raskar
- Li Shen
- Qiang Yang
- Tarik Taleb
- Dinh C. Nguyen
- F. I. H. Vincent Poor
In Artificial Intelligence
- Murali Annavaram
- Qiang Yang
- Chaoyang He
- Yang Liu
- S. Avestimehr
- Praneeth Vepakomma
- Abhishek Singh
- Ramesh Raskar
In Electrical and Electronic Engineering
- Dinh C. Nguyen
- Pubudu N. Pathirana
- Azzam Mourad
- Jinhyun So
- Aruna Prasad Seneviratne
- Jun Li
- Ming Ding
- Murali Annavaram
Citations in the corpus
- FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…but are not limited to, computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], finance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106, 107], robotics [108,…”
- Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “In B5G communication networks, there are new emerging applications such as blockchain techniques [293], [294], [295],”
- Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “For example, in [50], the authors adopt federated learning to enable privacy-preserving machine learning across a group of autonomous Drones-as-a-Service (DaaS).”
- Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “FL is also a very useful solution to replace traditional centralized ML approaches in traffic prediction tasks [10], [26] by running ML IoT Devices”