Auction Based Clustered Federated Learning in Mobile Edge Computing System
Not on the map. Why?
Abstract (arXiv)
In recent years, mobile clients' computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising distributed machine learning solution that uses local computing and local data to train the Artificial Intelligence (AI) model. Combining local computing and federated learning can train a powerful AI model under the premise of ensuring local data privacy while making full use of mobile clients' resources. However, the heterogeneity of local data, that is, Non-independent and identical distribution (Non-IID) and imbalance of local data size, may bring a bottleneck hindering the application of federated learning in mobile edge computing (MEC) system. Inspired by this, we propose a cluster-based clients selection method that can generate a federated virtual dataset that satisfies the global distribution to offset the impact of data heterogeneity and proved that the proposed scheme could converge to an approximate optimal solution. Based on the clustering method, we propose an auction-based clients selection scheme within each cluster that fully considers the system's energy heterogeneity and gives the Nash equilibrium solution of the proposed scheme for balance the energy consumption and improving the convergence rate. The simulation results show that our proposed selection methods and auction-based federated learning can achieve better performance with the Convolutional Neural Network model (CNN) under different data distributions.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 12 Mar 2021.
Builds on
18 of 28 references.
References · 18
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
- 2018Federated Learning with Non-IID Datacited by 1,099
- 2020Federated Learning with Matched Averagingcited by 564
- 2020An Efficient Framework for Clustered Federated Learningcited by 505
- 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
- 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
- 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
- 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
- 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242
6 more
- 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
- 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
- 2019Free-riders in Federated Learning: Attacks and Defensescited by 48
- 2018Multi-objective Evolutionary Federated Learningcited by 42
- 2019An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learningcited by 9
- 2020Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learningcited by 6
Built on by
Citing papers per year: 2021: 1.
1 paper.