Name
Title | ||
|---|---|---|
Adaptive Batchsize Selection And Gradient Compression For Wireless Federated Learning |
Abstract | ||
|---|---|---|
In wireless federated learning system, wireless communication and local computation have a significant impact on the learning latency due to the limited bandwidth and computing power of mobile devices. To reduce the learning latency, local stochastic gradient methods and gradient compression can be applied, which however would decrease the convergence rate. To tackle such issues, in this paper, the trade-off between the convergence rate and the learning latency is taken into account. We first formulate an optimization problem to maximize the convergence rate under the given training latency constraint via jointly optimizing the batchsize, compression ratio, and spectrum allocation. Then, by decomposing the problem into two subproblems, an adaptive algorithm is proposed to obtain the optimal solution. The results show that batchsize and compression ratio should be selected according to the computing power and channel state information of the devices to improve the convergence rate. Finally, experimental results are presented to verify the effectiveness of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/GLOBECOM42002.2020.9322122 | 2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) |
DocType | ISSN | Citations |
Conference | 2334-0983 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengli Liu | 1 | 3 | 1.74 |
| Guanding Yu | 2 | 1287 | 101.15 |
| Rui Yin | 3 | 129 | 11.38 |
| Jiantao Yuan | 4 | 0 | 0.68 |
| Fengzhong Qu | 5 | 196 | 19.02 |