Fetching the paper…
Reading the bibliography…
Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks.
D. Bertsekas, Nonlinear Programming . Athena Scientific, 1999
1999
Earlier work this paper cites.
A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity. part i. system description,” EEE Trans. Commun. , vol. 51, no. 11, pp. 1927–1938, 2003
2003
Earlier work this paper cites.
G. Kramer, I. Marić, and R. D. Yates, “Cooperative communications,” Found. Trends Netw. , vol. 1, no. 3, p. 271–425, Aug. 2006
2006
Earlier work this paper cites.
H. Y. Shutoy, D. Gunduz, E. Erkip, and Y. Wang, “Cooperative source and channel coding for wireless multimedia communications,” IEEE J. Sel. Top. Signal Process. , vol. 1, no. 2, pp. 295–307, 2007
2007
Earlier work this paper cites.
N. Michael, M. M. Zavlanos, V. Kumar, and G. J. Pappas, “Maintaining connectivity in mobile robot networks,” in Experimental Robotics , 2009
2009
Earlier work this paper cites.
M. M. Zavlanos, M. B. Egerstedt, and G. J. Pappas, “Graph-theoretic connectivity control of mobile robot networks,” Proc. IEEE , vol. 99, no. 9, pp. 1525–1540, Sep. 2011
2011
Earlier work this paper cites.
Y. Yan and Y. Mostofi, “Co-optimization of communication and motion planning of a robotic operation under resource constraints and in fading environments,” IEEE Trans. Wireless Commun. , vol. 12, no. 4, pp. 1562–1572, April 2013
2013
Earlier work this paper cites.
D. Gunduz, E. Erkip, A. Goldsmith, and H. V. Poor, “Reliable joint source-channel cooperative transmission over relay networks,” IEEE Trans. Inf. Theory , vol. 59, no. 4, pp. 2442–2458, 2013
2013
Earlier work this paper cites.
M. R. Akdeniz, Y. Liu, M. K. Samimi, S. Sun, S. Rangan, T. S. Rappaport, and E. Erkip, “Millimeter wave channel modeling and cellular capacity evaluation,” IEEE J. Sel. Areas Commun. , vol. 32, no. 6, pp. 1164–1179, June 2014
2014
Earlier work this paper cites.
S. Gil, S. Kumar, D. Katabi, and D. Rus, “Adaptive communication in multi-robot systems using directionality of signal strength,” Int. J. Rob. Res. , vol. 34, no. 7, pp. 946–968, 2015
2015
Earlier work this paper cites.
M. Gapeyenko, A. Samuylov, M. Gerasimenko, D. Moltchanov, S. Singh, E. Aryafar, S. Yeh, N. Himayat, S. Andreev, and Y. Koucheryavy, “Analysis of human-body blockage in urban millimeter-wave cellular communications,” in IEEE Int. Conf. Commun. (ICC) , May 2016, pp. 1–7
2016
Earlier work this paper cites.
K. Yuan, Q. Ling, and W. Yin, “On the convergence of decentralized gradient descent,” SIAM J. Optim. , 2016
2016
Earlier work this paper cites.
S. Li, M. A. Maddah-Ali, and A. S. Avestimehr, “Fundamental tradeoff between computation and communication in distributed computing,” in IEEE Int. Symp. Inf. Theory (ISIT) , 2016, pp. 1814–1818
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Int. Conf. Artif. Intell. Stat. (AISTATS) , vol. 54, Apr 2017, pp. 1273–1282
2017
Earlier work this paper cites.
M. Gapeyenko, A. Samuylov, M. Gerasimenko, D. Moltchanov, S. Singh, M. R. Akdeniz, E. Aryafar, N. Himayat, S. Andreev, and Y. Koucheryavy, “On the temporal effects of mobile blockers in urban millimeter-wave cellular scenarios,” IEEE Trans. Veh. Technol. , vol. 66, no. 11, pp. 10 124–10 138, Nov 2017
2017
Earlier work this paper cites.
X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu, “Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,” in NIPS , Dec. 2017
2017
Earlier work this paper cites.
Z. Jiang, A. Balu, C. Hegde, and S. Sarkar, “Collaborative deep learning in fixed topology networks,” in NIPS , Dec. 2017
2017
Earlier work this paper cites.
R. Tandon, Q. Lei, A. G. Dimakis, and N. Karampatziakis, “Gradient coding: Avoiding stragglers in distributed learning,” in 34th Int. Conf. Mach. Learn. , ser. Proceedings of Machine Learning Research, vol. 70. PMLR, Aug 2017, pp. 3368–3376
2017
Earlier work this paper cites.
H. Tang, X. Lian, M. Yan, C. Zhang, and J. Liu, “ d 2 d^{2} : Decentralized training over decentralized data,” in 35th Int. Conf. Mach. Learn. , vol. 80. PMLR, Jul 2018, pp. 4848–4856
2018
Earlier work this paper cites.
J. Zeng and W. Yin, “On nonconvex decentralized gradient descent,” IEEE Trans. Signal Process. , vol. 66, no. 11, pp. 2834–2848, June 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
K. Lee, M. Lam, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran, “Speeding up distributed machine learning using codes,” IEEE Trans. Inf. Theory , vol. 64, no. 3, pp. 1514–1529, 2018
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
M. Kamp, L. Adilova, J. Sicking, F. Hüger, P. Schlicht, T. Wirtz, and S. Wrobel, “Efficient decentralized deep learning by dynamic model averaging,” in Conf. Mach. Learn. Knowl. Discovery in Databases , 2019, pp. 393–409
2019
Cited alongside, same era.
2019
Cited alongside, same era.
M. Assran, N. Loizou, N. Ballas, and M. Rabbat, “Stochastic gradient push for distributed deep learning,” in 36th Int. Conf. Mach. Learn. PMLR, Jun 2019, pp. 344–353
2019
Cited alongside, same era.
2019
Cited alongside, same era.
D. Liu, G. Zhu, J. Zhang, and K. Huang, “Data-importance aware user scheduling for communication-efficient edge machine learning,” IEEE Trans. Cogn. Commun. Netw. , vol. 7, no. 1, pp. 265–278, 2021
2021
Later among the works it cites.
T. Sery, N. Shlezinger, K. Cohen, and Y. C. Eldar, “Over-the-air federated learning from heterogeneous data,” IEEE Transactions on Signal Processing , vol. 69, pp. 3796–3811, July 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Mohammadi Amiri and D. Gündüz, “Computation scheduling for distributed machine learning with straggling workers,” IEEE Trans. Signal Process. , vol. 67, no. 24, pp. 6270–6284, 2019
2019
Cited alongside, same era.
D. Gündüz, D. B. Kurka, M. Jankowski, M. M. Amiri, E. Ozfatura, and S. Sreekumar, “Communicate to learn at the edge,” IEEE Comm. Magazine , vol. 58, no. 12, pp. 14–19, 2020
2020
Cited alongside, same era.
W. Xia, T. Q. S. Quek, K. Guo, W. Wen, H. H. Yang, and H. Zhu, “Multi-armed bandit based client scheduling for federated learning,” IEEE Trans. Wireless Commun. , pp. 1–1, 2020
2020
Cited alongside, same era.
H. H. Yang, A. Arafa, T. Q. S. Quek, and H. Vincent Poor, “Age-based scheduling policy for federated learning in mobile edge networks,” in Proc. - ICASSP IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , 2020, pp. 8743–8747
2020
Cited alongside, same era.
M. M. Amiri and D. Gündüz, “Federated learning over wireless fading channels,” IEEE Trans. Wireless Comms. , vol. 19, no. 5, pp. 3546–3557, 2020
2020
Cited alongside, same era.
E. Ozfatura, S. Rini, and D. Gündüz, “Decentralized sgd with over-the-air computation,” in IEEE Glob. Commun. Conf , 2020
2020
Cited alongside, same era.
M. Yemini, S. Gil, and A. J. Goldsmith, “Exploiting local and cloud sensor fusion in intermittently connected sensor networks,” in IEEE Glob. Commun. Conf (Globecom) , December 2020
2020
Cited alongside, same era.
M. S. H. Abad, E. Ozfatura, D. Gündüz, and O. Ercetin, “Hierarchical federated learning across heterogeneous cellular networks,” in Proc. - ICASSP IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , 2020, pp. 8866–8870
2020
Cited alongside, same era.
M. M. Amiri, D. Gündüz, S. R. Kulkarni, and H. V. Poor, “Convergence of update aware device scheduling for federated learning at the wireless edge,” IEEE Trans. Wireless Comm. , vol. 20, no. 6, pp. 3643–3658, 2021
2021
Later among the works it cites.
M. Chen, D. Gündüz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, and H. V. Poor, “Distributed learning in wireless networks: Recent progress and future challenges,” IEEE J. Sel. Areas Commun. , vol. 39, no. 12, pp. 3579–3605, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
T. Vogels, L. He, A. Koloskova, S. P. Karimireddy, T. Lin, S. U. Stich, and M. Jaggi, “Relaysum for decentralized deep learning on heterogeneous data,” in Thirty-Fifth Conference on Neural Information Processing Systems , 2021
2021
Later among the works it cites.
T. Castiglia, A. Das, and S. Patterson, “Multi-level local SGD: Distributed SGD for heterogeneous hierarchical networks,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
F. P.-C. Lin, S. Hosseinalipour, S. S. Azam, C. G. Brinton, and N. Michelusi, “Semi-decentralized federated learning with cooperative d2d local model aggregations,” IEEE J. Sel. Areas Commun. , vol. 39, no. 12, pp. 3851–3869, 2021
2021
Later among the works it cites.
H. Yang, M. Fang, and J. Liu, “Achieving linear speedup with partial worker participation in non-IID federated learning,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
M. E. Ozfatura, J. Zhao, and D. Gündüz, “Fast federated edge learning with overlapped communication and computation and channel-aware fair client scheduling,” in IEEE Int. Workshop Signal Process. Adv. in Wireless Commun. (SPAWC) , 2021, pp. 311–315
2021
Later among the works it cites.
S. Prakash, S. Dhakal, M. R. Akdeniz, Y. Yona, S. Talwar, S. Avestimehr, and N. Himayat, “Coded computing for low-latency federated learning over wireless edge networks,” IEEE J. Sel. Areas Commun. , vol. 39, no. 1, pp. 233–250, 2021
2021
Later among the works it cites.
M. Yemini, R. Saha, E. Ozfatura, D. Gündüz, and A. J. Goldsmith, “Semi-decentralized federated learning with collaborative relaying,” in IEEE Int. Symp. Inf. Theory (ISIT) , 2022
2022
Closest in time.
K. Cohen, T. Gafni, and Y. C. Eldar, “CoBAAF: controlled bayesian air aggregation federated learning from heterogeneous data,” in 58th Annual Allerton Conference on Communication, Control, and Computing , Sep. 2022
2022
Closest in time.
——, “Cloud-cluster architecture for detection in intermittently connected sensor networks,” accepted for publication in the IEEE Trans. Wirel. Commun., 2022
2022
Closest in time.
R. Saha, S. Rini, M. Rao, and A. J. Goldsmith, “Decentralized optimization over noisy, rate-constrained networks: Achieving consensus by communicating differences,” IEEE J. Sel. Areas Commun. , vol. 40, no. 2, pp. 449–467, 2022
2022
Closest in time.
M. Yemini, A. Nedić, S. Gil, and A. J. Goldsmith, “Resilience to malicious activity in distributed optimization for cyberphysical systems,” in accepted to the 2022 IEEE Conference on Decision and Control (CDC) , 2022
2022
Closest in time.
W. Y. B. Lim, J. S. Ng, Z. Xiong, J. Jin, Y. Zhang, D. Niyato, C. Leung, and C. Miao, “Decentralized edge intelligence: A dynamic resource allocation framework for hierarchical federated learning,” IEEE Trans. Parallel Distrib. Syst. , vol. 33, no. 3, pp. 536–550, 2022
2022
Closest in time.
Y. Guo, Y. Sun, R. Hu, and Y. Gong, “Hybrid local SGD for federated learning with heterogeneous communications,” in International Conference on Learning Representations , 2022
2022
Closest in time.
E. Ozfatura, D. Gündüz, and H. V. Poor, “Collaborative learning over wireless networks: An introductory overview,” Machine Learning and Wireless Communications, Cambridge University Press, 2022
2022
Closest in time.