Name
Abstract | ||
|---|---|---|
Federated learning opens a number of research opportunities due to its high communication efficiency in distributed training problems within a star network. In this paper, we focus on improving the communication efficiency for fully decentralized federated learning (DFL) over a graph, where the algorithm performs local updates for several iterations and then enables communications among the nodes. In such a way, the communication rounds of exchanging the common interest of parameters can be saved significantly without loss of optimality of the solutions. Multiple numerical simulations based on large, real- world electronic health record databases showcase the superiority of the decentralized federated learning compared with classic methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CISS48834.2020.1570617414 | 2020 54th Annual Conference on Information Sciences and Systems (CISS) |
Keywords | DocType | ISBN |
decentralized federated learning (DFL),communication efficiency,health record databases,heterogeneous networks,non-convex optimization | Conference | 978-1-7281-8831-7 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songtao Lu | 1 | 84 | 19.52 |
| Yawen Zhang | 2 | 0 | 0.34 |
| Yunlong Wang | 3 | 0 | 1.35 |