1 |
KONEČNÝ J, MCMAHAN H B, RAMAGE D, et al. Federated optimization: distributed machine learning for on-device intelligence[EB/OL]. (2016-10-08) [2021-06-12]..
|
2 |
MCMAHAN B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data[C]// Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. New York: JMLR.org, 2017:1273-1282.
|
3 |
KONEČNÝ J, McMAHAN H B, YU F X, et al. Federated learning: strategies for improving communication efficiency[EB/OL]. (2017-10-30) [2021-06-17]..
|
4 |
RIBERO M, VIKALO H. Communication-efficient federated learning via optimal client sampling[EB/OL]. (2020-10-14) [2021-06-08].. 10.48550/arXiv.2007.15197
|
5 |
CHEN Y, SUN X Y, JIN Y C. Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(10): 4229-4238. 10.1109/tnnls.2019.2953131
|
6 |
WANG L P, WANG W, LI B. CMFL: mitigating communication overhead for federated learning[C]// Proceedings of the IEEE 39th International Conference on Distributed Computing Systems. Piscataway: IEEE, 2019: 954-964. 10.1109/icdcs.2019.00099
|
7 |
JEONG E, OH S, KIM H, et al. Communication-efficient on-device machine learning: federated distillation and augmentation under non-IID private data[EB/OL]. (2018-11-28) [2021-06-16].. 10.1109/mis.2020.3028613
|
8 |
XU J J, DU W L, JIN Y C, et al. Ternary compression for communication-efficient federated learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(3): 1162-1176. 10.1109/tnnls.2020.3041185
|
9 |
HE Y, ZENK M, FRITZ M. CosSGD: nonlinear quantization for communication-efficient federated learning[EB/OL]. (2020-12-15) [2021-06-20].. 10.48550/arXiv.2012.08241
|
10 |
CHEN R, LI L, XUE K P, et al. To talk or to work: energy efficient federated learning over mobile devices via the weight quantization and 5G transmission co-design[EB/OL]. (2020-12-21) [2021-06-20].. 10.48550/arXiv.2012.11070
|
11 |
LI L, SHI D, HOU R H, et al. To talk or to work: flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices[C]// Proceedings of the 2021 IEEE Conference on Computer Communications. Piscataway: IEEE, 2021: 1-10. 10.1109/infocom42981.2021.9488839
|
12 |
SATTLER F, WIEDEMANN S, MÜLLER K R, et al. Sparse binary compression: towards distributed deep learning with minimal communication[C]// Proceedings of the 2019 International Joint Conference on Neural Networks. Piscataway: IEEE, 2019: 1-8. 10.1109/ijcnn.2019.8852172
|
13 |
SATTLER F, WIEDEMANN S, MÜLLER K R, et al. Robust and communication-efficient federated learning from non-IID data[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(9): 3400-3413. 10.1109/tnnls.2019.2944481
|
14 |
CALDAS S, KONEČNÝ J, McMAHAN H B, et al. Expanding the reach of federated learning by reducing client resource requirements[EB/OL]. (2019-01-08) [2021-06-23].. 10.48550/arXiv.1812.07210
|
15 |
JEON Y S, AMIRI M M, LI J, et al. A compressive sensing approach for federated learning over massive MIMO communication systems[J]. IEEE Transactions on Wireless Communications, 2021, 20(3): 1990-2004. 10.1109/twc.2020.3038407
|
16 |
FAN X, WANG Y, HUO Y, et al. 1‑bit compressive sensing for efficient federated learning over the air[EB/OL]. (2021-03-30) [2021-06-22].. 10.1109/iccworkshops50388.2021.9473872
|
17 |
PAN Y J, PAN C H, YANG Z H, et al. Resource allocation for D2D communications underlaying a NOMA-based cellular network[J]. IEEE Wireless Communications Letters, 2018, 7(1): 130-133. 10.1109/lwc.2017.2759114
|
18 |
CHEN M Z, YANG Z H, SAAD W, et al. A joint learning and communications framework for federated learning over wireless networks[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 269-283. 10.1109/twc.2020.3024629
|
19 |
XI Y, BURR A, WEI J B, et al. A general upper bound to evaluate packet error rate over quasi-static fading channels[J]. IEEE Transactions on Wireless Communications, 2011, 10(5): 1373-1377. 10.1109/twc.2011.012411.100787
|
20 |
KUHN H W. The Hungarian method for the assignment problem[J]. Naval Research Logistics Quarterly, 1955, 2(1/2): 83-97. 10.1002/nav.3800020109
|
21 |
BOUFOUNOS P T, BARANIUK R G. 1‑bit compressive sensing[C]// Proceedings of the 42nd Annual Conference on Information Sciences and Systems. Piscataway: IEEE, 2008: 16-21. 10.1109/ciss.2008.4558487
|
22 |
JACQUES L, LASKA J N, BOUFOUNOS P T, et al. Robust 1‑bit compressive sensing via binary stable embeddings of sparse vectors[J]. IEEE Transactions on Information Theory, 2013, 59(4): 2082-2102. 10.1109/tit.2012.2234823
|
23 |
KOEP N, MATHAR R. Binary iterative hard thresholding for frequency-sparse signal recovery[C]// Proceedings of the 21th International ITG Workshop on Smart Antennas. Berlin: VDE VERLAG, 2017: 1-7. 10.1109/ssp.2018.8450728
|
24 |
LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. 10.1109/5.726791
|
25 |
HENNIG C, KUTLUKAYA M. Some thoughts about the design of loss functions[J]. REVSTAT — Statistical Journal, 2007, 5(1): 19-39.
|