A Learning-Based Incentive Mechanism for Federated Learning
Hong Kong Polytechnic University · University of Aizu · +2 more institutions
Abstract
Internet of Things (IoT) generates large amounts of data at the network edge. Machine learning models are often built on these data, to enable the detection, classification, and prediction of the future events. Due to network bandwidth, storage, and especially privacy concerns, it is often impossible to send all the IoT data to the data center for centralized model training. To address these issues, federated learning has been proposed to let nodes use the local data to train models, which are then aggregated to synthesize a global model. Most of the existing work has focused on designing learning algorithms with provable convergence time, but other issues, such as incentive mechanism, are unexplored. Although…
Citation impact
- FWCI
- 48.96
- Percentile
- 100%
- References
- 54
Authors
5Topics & keywords
- Computer science
- Incentive
- Reinforcement learning
- Enhanced Data Rates for GSM Evolution
- Edge computing
- Edge device
- Artificial intelligence
- Machine learning