Built on

M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” 2019. [Online]. Available: http://arxiv.org/abs/1902.00146
Original
1902
Earlier work this paper cites.
F. Sattler, S. Wiedemann, K. Müller, and W. Samek, “Robust and communication-efficient federated learning from Non-IID data,” 2019. [Online]. Available: http://arxiv.org/abs/1903.02891
Original
1903
Earlier work this paper cites.
J. Kang, Z. Xiong, D. Niyato, H. Yu, Y. Liang, and D. I. Kim, “Incentive design for efficient federated learning in mobile networks: A contract theory approach,” 2019. [Online]. Available: http://arxiv.org/abs/1905.07479
Original
1905
Earlier work this paper cites.
J. Ahn, O. Simeone, and J. Kang, “Wireless federated distillation for distributed edge learning with heterogeneous data,” 2019. [Online]. Available: http://arxiv.org/abs/1907.02745
Original
1907
Earlier work this paper cites.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” 2019. [Online]. Available: https://arxiv.org/abs/1908.07873
Original
1908
Earlier work this paper cites.
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,” 2019. [Online]. Available: https://arxiv.org/abs/1909.07972
Original
1909
Earlier work this paper cites.
B. R. Marks and G. P. Wright, “A general inner approximation algorithm for nonconvex mathematical programs,” Operations Research , vol. 26, no. 4, pp. 681–683, 1978
1978
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S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory . Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1993
1993
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex Optimization . New York, NY: Cambridge University Press, 2004
2004
Earlier work this paper cites.
A. P. Miettinen and J. K. Nurminen, “Energy efficiency of mobile clients in cloud computing,” in Proc. 2nd USENIX Conf. HotCloud , 2010
2010
Earlier work this paper cites.
M. Jaggi, V. Smith, M. Takáč, J. Terhorst, S. Krishnan, T. Hofmann, and M. I. Jordan, “Communication-efficient distributed dual coordinate ascent,” in Proc. 27th Int. Conf. Neural Inform. Process. Syst. (NIPS) , vol. 2, 2014, pp. 3068–3076
2014
Earlier work this paper cites.
T. L. Marzetta, E. G. Larsson, H. Yang, and H. Q. Ngo, Fundamentals of Massive MIMO . Cambridge University Press, 2016
2016
Earlier work this paper cites.

Similar

H. Q. Ngo, L. Tran, T. Q. Duong, M. Matthaiou, and E. G. Larsson, “On the total energy efficiency of cell-free massive MIMO,” IEEE Trans. Green Commun. Netw. , vol. 2, no. 1, pp. 25–39, Mar. 2018
Original
2016
Cited alongside, same era.
Qualcomm, “We are making on-device AI ubiquitous,” https://www.qualcomm.com/news/onq/2017/08/16/we-are-making-device-ai-ubiquitous, 2017
2017
Cited alongside, same era.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proc. Int. Conf. Artificial Intell. Statist. , vol. 54, Fort Lauderdale, FL, USA, Apr. 2017, pp. 1273–1282
2017
Cited alongside, same era.
H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson, and T. L. Marzetta, “Cell-free massive MIMO versus small cells,” IEEE Trans. Wireless Commun. , vol. 16, no. 3, pp. 1834–1850, Mar. 2017
2017
Cited alongside, same era.

Then

A. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” 2018. [Online]. Available: http://arxiv.org/abs/1811.03604
Original
2018
Later among the works it cites.
A. Liu, V. K. N. Lau, and M. Zhao, “Online successive convex approximation for two-stage stochastic nonconvex optimization,” IEEE Trans. Signal Process. , vol. 66, no. 22, pp. 5941–5955, Nov. 2018
2018
Later among the works it cites.
H. Zhu and Y. Jin, “Multi-objective evolutionary federated learning,” 2018. [Online]. Available: http://arxiv.org/abs/1812.07478
Original
2018
Later among the works it cites.
T. T. Vu, D. T. Ngo, M. N. Dao, S. Durrani, D. H. N. Nguyen, and R. H. Middleton, “Energy efficiency maximization for downlink cloud radio access networks with data sharing and data compression,” IEEE Trans. Wireless Commun. , vol. 17, no. 8, pp. 4955–4970, Aug. 2018
2018
Later among the works it cites.

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Reading the bibliography…

2019

Cell-Free Massive MIMO for Wireless Federated Learning

Vu, Tung T., Ngo, Duy T., Tran, Nguyen H. et al.

Understand

This paper proposes a novel scheme for cell-free massive multiple-input multiple-output (CFmMIMO) networks to support any federated learning (FL) framework.

This scheme allows each instead of all the iterations of the FL framework to happen in a large-scale coherence time to guarantee a stable operation of an FL process.
To show how to optimize the FL performance using this proposed scheme, we consider an existing FL framework as an example and target FL training time minimization for this framework.
An optimization problem is then formulated to jointly optimize the local accuracy, transmit power, data rate, and users' processing frequency.

Built on

M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” 2019. [Online]. Available: http://arxiv.org/abs/1902.00146
Original
1902
Earlier work this paper cites.
F. Sattler, S. Wiedemann, K. Müller, and W. Samek, “Robust and communication-efficient federated learning from Non-IID data,” 2019. [Online]. Available: http://arxiv.org/abs/1903.02891
Original
1903
Earlier work this paper cites.
J. Kang, Z. Xiong, D. Niyato, H. Yu, Y. Liang, and D. I. Kim, “Incentive design for efficient federated learning in mobile networks: A contract theory approach,” 2019. [Online]. Available: http://arxiv.org/abs/1905.07479
Original
1905
Earlier work this paper cites.
J. Ahn, O. Simeone, and J. Kang, “Wireless federated distillation for distributed edge learning with heterogeneous data,” 2019. [Online]. Available: http://arxiv.org/abs/1907.02745
Original
1907
Earlier work this paper cites.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” 2019. [Online]. Available: https://arxiv.org/abs/1908.07873
Original
1908
Earlier work this paper cites.
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A joint learning and communications framework for federated learning over wireless networks,” 2019. [Online]. Available: https://arxiv.org/abs/1909.07972
Original
1909
Earlier work this paper cites.
B. R. Marks and G. P. Wright, “A general inner approximation algorithm for nonconvex mathematical programs,” Operations Research , vol. 26, no. 4, pp. 681–683, 1978
1978
Earlier work this paper cites.
S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory . Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1993
1993
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex Optimization . New York, NY: Cambridge University Press, 2004
2004
Earlier work this paper cites.
A. P. Miettinen and J. K. Nurminen, “Energy efficiency of mobile clients in cloud computing,” in Proc. 2nd USENIX Conf. HotCloud , 2010
2010
Earlier work this paper cites.
M. Jaggi, V. Smith, M. Takáč, J. Terhorst, S. Krishnan, T. Hofmann, and M. I. Jordan, “Communication-efficient distributed dual coordinate ascent,” in Proc. 27th Int. Conf. Neural Inform. Process. Syst. (NIPS) , vol. 2, 2014, pp. 3068–3076
2014
Earlier work this paper cites.
T. L. Marzetta, E. G. Larsson, H. Yang, and H. Q. Ngo, Fundamentals of Massive MIMO . Cambridge University Press, 2016
2016
Earlier work this paper cites.

Similar

H. Q. Ngo, L. Tran, T. Q. Duong, M. Matthaiou, and E. G. Larsson, “On the total energy efficiency of cell-free massive MIMO,” IEEE Trans. Green Commun. Netw. , vol. 2, no. 1, pp. 25–39, Mar. 2018
Original
2016
Cited alongside, same era.
Qualcomm, “We are making on-device AI ubiquitous,” https://www.qualcomm.com/news/onq/2017/08/16/we-are-making-device-ai-ubiquitous, 2017
2017
Cited alongside, same era.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proc. Int. Conf. Artificial Intell. Statist. , vol. 54, Fort Lauderdale, FL, USA, Apr. 2017, pp. 1273–1282
2017
Cited alongside, same era.
H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson, and T. L. Marzetta, “Cell-free massive MIMO versus small cells,” IEEE Trans. Wireless Commun. , vol. 16, no. 3, pp. 1834–1850, Mar. 2017
2017
Cited alongside, same era.

Then

A. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, “Federated learning for mobile keyboard prediction,” 2018. [Online]. Available: http://arxiv.org/abs/1811.03604
Original
2018
Later among the works it cites.
A. Liu, V. K. N. Lau, and M. Zhao, “Online successive convex approximation for two-stage stochastic nonconvex optimization,” IEEE Trans. Signal Process. , vol. 66, no. 22, pp. 5941–5955, Nov. 2018
2018
Later among the works it cites.
H. Zhu and Y. Jin, “Multi-objective evolutionary federated learning,” 2018. [Online]. Available: http://arxiv.org/abs/1812.07478
Original
2018
Later among the works it cites.
T. T. Vu, D. T. Ngo, M. N. Dao, S. Durrani, D. H. N. Nguyen, and R. H. Middleton, “Energy efficiency maximization for downlink cloud radio access networks with data sharing and data compression,” IEEE Trans. Wireless Commun. , vol. 17, no. 8, pp. 4955–4970, Aug. 2018
2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

C. Ma, J. Konec̆ný, M. Jaggi, V. Smith, M. I. Jordan, P. Richtárik, and M. Takáč, “Distributed optimization with arbitrary local solvers,” Optimization Methods Software , vol. 32, no. 4, pp. 813–848, Jul. 2017

2017

Cited alongside, same era.

J. Konec̆ný, Z. Qu, and P. Richtárik, “Semi-stochastic coordinate descent,” Optimization Methods and Software , vol. 32, no. 5, pp. 993–1005, 2017

2017

Cited alongside, same era.

E. Björnson, J. Hoydis, and L. Sanguinetti, “Massive MIMO networks: Spectral, energy, and hardware efficiency,” Found. Trends. Signal Process. , vol. 11, no. 3-4, pp. 154–655, 2017

2017

Cited alongside, same era.

V. D. Nguyen, T. Q. Duong, H. D. Tuan, O. S. Shin, and H. V. Poor, “Spectral and energy efficiencies in full-duplex wireless information and power transfer,” IEEE Trans. Commun. , vol. 65, no. 5, pp. 2220–2233, May 2017

2017

Cited alongside, same era.

F. D. Calabrese, L. Wang, E. Ghadimi, G. Peters, L. Hanzo, and P. Soldati, “Learning radio resource management in RANs: Framework, opportunities, and challenges,” IEEE Commun. Mag. , vol. 56, no. 9, pp. 138–145, Sep. 2018

2018

Cited alongside, same era.

Gartner, “Gartner highlights 10 uses for AI-powered smartphones,” https://www.gartner.com/en/newsroom/press-releases/2018-03-20-gartner-highlights-10-uses-for-ai-powered-smartphones, 2018

2018

Cited alongside, same era.

G. Zhu, D. Liu, Y. Du, C. You, J. Zhang, and K. Huang, “Towards an intelligent edge: Wireless communication meets machine learning,” 2018. [Online]. Available: http://arxiv.org/abs/1809.00343

Original

2018

Cited alongside, same era.

M. Chen and Y. Hao, “Task offloading for mobile edge computing in software defined ultra-dense network,” IEEE J. Sel. Areas Commun. , vol. 36, no. 3, pp. 587–597, Mar. 2018

2018

Cited alongside, same era.

K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y. A. Zhang, “The roadmap to 6G: AI empowered wireless networks,” IEEE Commun. Mag. , vol. 57, no. 8, pp. 84–90, Aug. 2019

2019

Closest in time.

J. Dong, M. Noreikis, Y. Xiao, and A. Ylä-Jääski, “Vinav: A vision-based indoor navigation system for smartphones,” IEEE Trans. Mobile Comput. , vol. 18, no. 6, pp. 1461–1475, Jun. 2019

2019

Closest in time.

H. A. Alameddine, S. Sharafeddine, S. Sebbah, S. Ayoubi, and C. Assi, “Dynamic task offloading and scheduling for low-latency iot services in multi-access edge computing,” IEEE J. Sel. Areas Commun, , vol. 37, no. 3, pp. 668–682, Mar. 2019

2019

Closest in time.

N. H. Tran, W. Bao, A. Zomaya, N. Minh N.H., and C. S. Hong, “Federated learning over wireless networks: Optimization model design and analysis,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM) , Apr. 2019, pp. 1387–1395

2019

Closest in time.

S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan, “Adaptive federated learning in resource constrained edge computing systems,” IEEE J. Sel. Areas Commun. , vol. 37, no. 6, pp. 1205–1221, Jun. 2019

2019

Closest in time.

J. Zhang, S. Chen, Y. Lin, J. Zheng, B. Ai, and L. Hanzo, “Cell-free massive MIMO: A new next-generation paradigm,” IEEE Access , vol. 7, pp. 99 878–99 888, 2019

2019

Closest in time.

K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation,” IEEE Trans. Wireless Commun. , vol. 19, no. 3, pp. 2022–2035, 2020

2020

Closest in time.