Name
Abstract | ||
|---|---|---|
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data privacy. Unfortunately, the learning performance of FEEL may be compromised due to limited training data in a single edge cluster. In this paper, we investigate a novel framework of FEEL, namely semi-decentralized federated edge learning (SD-FEEL). By allowing model aggregation across different edge clusters, SD-FEEL enjoys the benefit of FEEL in reducing the training latency, while improving the learning performance by accessing richer training data from multiple edge clusters. A training algorithm for SD-FEEL with three main procedures in each round is presented, including local model updates, intra-cluster and inter-cluster model aggregations, which is proved to converge on non-independent and identically distributed (non-IID) data. We also characterize the interplay between the network topology of the edge servers and the communication overhead of inter-cluster model aggregation on the training performance. Experiment results corroborate our analysis and demonstrate the effectiveness of SD-FFEL in achieving faster convergence than traditional federated learning architectures. Besides, guidelines on choosing critical hyper-parameters of the training algorithm are also provided. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/WCNC51071.2022.9771904 | 2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) |
Keywords | DocType | ISSN |
Federated learning, non-IID data, distributed machine learning, communication efficiency | Conference | 1525-3511 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchang Sun | 1 | 0 | 1.01 |
| Jiawei Shao | 2 | 0 | 1.01 |
| Yuyi Mao | 3 | 807 | 27.39 |
| Jun Zhang | 4 | 3772 | 190.36 |