Fetching the paper…
Reading the bibliography…
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server.
K. Krishna and M. N. Murty, “Genetic k-means algorithm,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , vol. 29, no. 3, pp. 433–439, 1999
1999
Earlier work this paper cites.
S. Martello, D. Pisinger, and P. Toth, “New trends in exact algorithms for the 0–1 knapsack problem,” Euro. J. Operational Res. , vol. 123, no. 2, pp. 325–332, 2000
2000
Earlier work this paper cites.
T. Kuno, “A branch-and-bound algorithm for maximizing the sum of several linear ratios,” J. Glob. Optimization , vol. 22, no. 1-4, pp. 155–174, 2002
2002
Earlier work this paper cites.
S. Schaible and J. Shi, “Fractional programming: the sum-of-ratios case,” Optimization Methods Software , vol. 18, no. 2, pp. 219–229, 2003
2003
Earlier work this paper cites.
F. Liese and I. Vajda, “On divergences and informations in statistics and information theory,” IEEE Trans. Inf. Theory , vol. 52, no. 10, pp. 4394–4412, 2006
2006
Earlier work this paper cites.
A. D. Ribas, J. G. Colonna, C. M. Figueiredo, and E. F. Nakamura, “Similarity clustering for data fusion in wireless sensor networks using k-means,” in The 2012 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2012, pp. 1–7
2012
Earlier work this paper cites.
T. Jeske, “Floating car data from smartphones: What google and waze know about you and how hackers can control traffic,” Proc. BlackHat Euro. , pp. 1–12, 2013
2013
Earlier work this paper cites.
2016
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning . MIT press, 2016
2016
Earlier work this paper cites.
S. Diamond and S. Boyd, “CVXPY: A Python-embedded modeling language for convex optimization,” J. Machine Learn. Res. , vol. 17, no. 83, pp. 1–5, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
H. 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. Artif. Intell. Stat. (AISTATS) , 2017
2017
Earlier work this paper cites.
M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Commun. Surv. Tut. , vol. 20, no. 4, pp. 2923–2960, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
R. Morabito, R. Petrolo, V. Loscri, and N. Mitton, “LEGIoT: A lightweight edge gateway for the internet of things,” Future Gen. Comput. Syst. , vol. 81, pp. 1–15, 2018
2018
Earlier work this paper cites.
Z. Li, Q. Chen, and V. Koltun, “Combinatorial optimization with graph convolutional networks and guided tree search,” in Proc. Adv. Neural Inf. Process. Syst. , 2018, pp. 539–548
2018
Earlier work this paper cites.
F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” IEEE Trans. Neural Netw. Learn. Syst. , 2019
2019
Earlier work this paper cites.
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. Selec. Areas Commun. , vol. 37, no. 6, pp. 1205–1221, 2019
2019
Earlier work this paper cites.
N. H. Tran, W. Bao, A. Zomaya, N. M. NH, and C. S. Hong, “Federated learning over wireless networks: Optimization model design and analysis,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM) , 2019, pp. 1387–1395
2019
Earlier work this paper cites.
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-iid data,” in International Conference on Learning Representations , 2019
2019
Earlier work this paper cites.
S. Heinrich, “Flash memory in the emerging age of autonomy,” https://www.flashmemorysummit.com/English/Collaterals/Proceedings/2017/20170808_FT12_Heinrich.pdf
2020
Earlier work this paper cites.
S. Niknam, H. S. Dhillon, and J. H. Reed, “Federated learning for wireless communications: Motivation, opportunities, and challenges,” IEEE Commun. Mag. , vol. 58, no. 6, pp. 46–51, 2020
2020
Earlier work this paper cites.
S. Hosseinalipour, C. G. Brinton, V. Aggarwal, H. Dai, and M. Chiang, “From federated to fog learning: Distributed machine learning over heterogeneous wireless networks,” IEEE Commun. Mag. , vol. 58, no. 12, pp. 41–47, 2020
2020
Earlier work this paper cites.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Process. Mag. , vol. 37, no. 3, pp. 50–60, 2020
2020
Earlier work this paper cites.
T. Huang, B. Ye, Z. Qu, B. Tang, L. Xie, and S. Lu, “Physical-layer arithmetic for federated learning in uplink mu-mimo enabled wireless networks,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM) , 2020, pp. 1221–1230
2020
Earlier work this paper cites.
W. Sun, S. Lei, L. Wang, Z. Liu, and Y. Zhang, “Adaptive federated learning and digital twin for industrial internet of things,” IEEE Transactions on Industrial Informatics , vol. 17, no. 8, pp. 5605–5614, 2020
2020
Cited alongside, same era.
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learning,” in International conference on machine learning . PMLR, 2020, pp. 5132–5143
2020
Cited alongside, same era.
Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, “Energy efficient federated learning over wireless communication networks,” IEEE Transactions on Wireless Communications , vol. 20, no. 3, pp. 1935–1949, 2020
2020
Cited alongside, same era.
H. H. Yang, Z. Liu, T. Q. S. Quek, and H. V. Poor, “Scheduling policies for federated learning in wireless networks,” IEEE Trans. Commun. , vol. 68, no. 1, pp. 317–333, 2020
2020
Cited alongside, same era.
M. Wu, F. R. Yu, and P. X. Liu, “Intelligence networking for autonomous driving in beyond 5g networks with multi-access edge computing,” IEEE Transactions on Vehicular Technology , vol. 71, no. 6, pp. 5853–5866, 2022
2022
Later among the works it cites.
X. Li, G. Feng, Y. Sun, S. Qin, and Y. Liu, “A unified framework for joint sensing and communication in resource constrained mobile edge networks,” IEEE Transactions on Mobile Computing , 2022
2022
Later among the works it cites.
S. Lu, Z. Gao, Q. Xu, C. Jiang, A. Zhang, and X. Wang, “Class-imbalance privacy-preserving federated learning for decentralized fault diagnosis with biometric authentication,” IEEE Transactions on Industrial Informatics , vol. 18, no. 12, pp. 9101–9111, 2022
2022
Later among the works it cites.
S. Zehtabi, S. Hosseinalipour, and C. G. Brinton, “Decentralized event-triggered federated learning with heterogeneous communication thresholds,” in 2022 IEEE 61st Conference on Decision and Control (CDC) . IEEE, 2022, pp. 4680–4687
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Shi, S. Zhou, and Z. Niu, “Device scheduling with fast convergence for wireless federated learning,” in ICC 2020-2020 IEEE International Conference on Communications (ICC) . IEEE, 2020, pp. 1–6
2020
Cited alongside, same era.
W. Xia, T. Q. Quek, K. Guo, W. Wen, H. H. Yang, and H. Zhu, “Multi-armed bandit based client scheduling for federated learning,” IEEE Transactions on Wireless Communications , vol. 19, no. 11, pp. 7108–7123, 2020
2020
Cited alongside, same era.
J. Ren, Y. He, D. Wen, G. Yu, K. Huang, and D. Guo, “Scheduling for cellular federated edge learning with importance and channel awareness,” IEEE Transactions on Wireless Communications , vol. 19, no. 11, pp. 7690–7703, 2020
2020
Cited alongside, same era.
H. Wang, Z. Kaplan, D. Niu, and B. Li, “Optimizing federated learning on non-iid data with reinforcement learning,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM) , 2020, pp. 1698–1707
2020
Cited alongside, same era.
J. Xie, D. Guo, X. Shi, H. Cai, C. Qian, and H. Chen, “A fast hybrid data sharing framework for hierarchical mobile edge computing,” in IEEE INFOCOM 2020-IEEE Conference on Computer Communications . IEEE, 2020, pp. 2609–2618
2020
Cited alongside, same era.
K. Jacksi, R. K. Ibrahim, S. R. Zeebaree, R. R. Zebari, and M. A. Sadeeq, “Clustering documents based on semantic similarity using hac and k-mean algorithms,” in 2020 International Conference on Advanced Science and Engineering (ICOASE) . IEEE, 2020, pp. 205–210
2020
Cited alongside, same era.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE Trans. Neural Netw. Learn. Syst. , 2020
2020
Cited alongside, same era.
“Dstat: Versatile resource statistics tool,” http://dag.wiee.rs/home-made/dstat/
2020
Cited alongside, same era.
2022
Later among the works it cites.
S. Wang, S. Hosseinalipour, M. Gorlatova, C. G. Brinton, and M. Chiang, “Uav-assisted online machine learning over multi-tiered networks: A hierarchical nested personalized federated learning approach,” IEEE Transactions on Network and Service Management , 2022
2022
Later among the works it cites.
Y. Jee Cho, J. Wang, and G. Joshi, “Towards understanding biased client selection in federated learning,” in Proceedings of The 25th International Conference on Artificial Intelligence and Statistics , ser. Proceedings of Machine Learning Research, G. Camps-Valls, F. J. R. Ruiz, and I. Valera, Eds., vol. 151. PMLR, 28–30 Mar 2022, pp. 10 351–10 375
2022
Later among the works it cites.
Z. Lin, H. Liu, and Y.-J. A. Zhang, “Relay-assisted cooperative federated learning,” IEEE Transactions on Wireless Communications , vol. 21, no. 9, pp. 7148–7164, 2022
2022
Later among the works it cites.
L. Barbieri, S. Savazzi, M. Brambilla, and M. Nicoli, “Decentralized federated learning for extended sensing in 6g connected vehicles,” Vehicular Communications , vol. 33, p. 100396, 2022
2022
Later among the works it cites.
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
Later among the works it cites.
C. Li, X. Zeng, M. Zhang, and Z. Cao, “Pyramidfl: A fine-grained client selection framework for efficient federated learning,” in Proceedings of the 28th Annual International Conference on Mobile Computing And Networking , 2022, pp. 158–171
2022
Later among the works it cites.
D. Wen, K.-J. Jeon, and K. Huang, “Federated dropout—a simple approach for enabling federated learning on resource constrained devices,” IEEE wireless communications letters , vol. 11, no. 5, pp. 923–927, 2022
2022
Later among the works it cites.
S. Wagle, S. Hosseinalipour, N. Khosravan, M. Chiang, and C. G. Brinton, “Embedding alignment for unsupervised federated learning via smart data exchange,” in GLOBECOM 2022-2022 IEEE Global Communications Conference . IEEE, 2022, pp. 492–497
2022
Later among the works it cites.
A. Gupta, S. Misra, N. Pathak, and D. Das, “Fedcare: Federated learning for resource-constrained healthcare devices in iomt system,” IEEE Transactions on Computational Social Systems , 2023
2023
Closest in time.
C. Chang, J. Zhang, K. Zhang, W. Zhong, X. Peng, S. Li, and L. Li, “Bev-v2x: Cooperative birds-eye-view fusion and grid occupancy prediction via v2x-based data sharing,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Closest in time.
S. Wang, S. Hosseinalipour, V. Aggarwal, C. G. Brinton, D. J. Love, W. Su, and M. Chiang, “Towards cooperative federated learning over heterogeneous edge/fog networks,” IEEE Communications Magazine , 2023
2023
Closest in time.
S. Hosseinalipour, S. Wang, N. Michelusi, V. Aggarwal, C. G. Brinton, D. J. Love, and M. Chiang, “Parallel successive learning for dynamic distributed model training over heterogeneous wireless networks,” IEEE/ACM Transactions on Networking , 2023
2023
Closest in time.
T. Wu, Y. Qu, C. Liu, Y. Jing, F. Wu, H. Dai, C. Dong, and J. Cao, “Joint edge aggregation and association for cost-efficient multi-cell federated learning,” in IEEE INFOCOM 2023-IEEE Conference on Computer Communications . IEEE, 2023, pp. 1–10
2023
Closest in time.
F. Dou, J. Lu, T. Zhu, and J. Bi, “On-device indoor positioning: A federated reinforcement learning approach with heterogeneous devices,” IEEE Internet of Things Journal , 2023
2023
Closest in time.
J. Zhang, W. Liu, Y. He, Z. He, and M. Guizani, “Semi-asynchronous model design for federated learning in mobile edge networks,” IEEE Transactions on Vehicular Technology , 2023
2023
Closest in time.
Y. Li, W. Liang, J. Li, X. Cheng, D. Yu, A. Y. Zomaya, and S. Guo, “Energy-aware, device-to-device assisted federated learning in edge computing,” IEEE Transactions on Parallel and Distributed Systems , 2023
2023
Closest in time.
B. Ganguly, S. Hosseinalipour, K. T. Kim, C. G. Brinton, V. Aggarwal, D. J. Love, and M. Chiang, “Multi-edge server-assisted dynamic federated learning with an optimized floating aggregation point,” IEEE/ACM Transactions on Networking , 2023
2023
Closest in time.
M. M. Amiri and D. Gündüz, “Federated learning over wireless fading channels,” IEEE Transactions on Wireless Communications , vol. 19, no. 5, pp. 3546–3557, 2020
2023
Closest in time.
S. Wang, S. Hosseinalipour, and C. G. Brinton, “Multi-source to multi-target decentralized federated domain adaptation,” IEEE Transactions on Cognitive Communications and Networking , 2024
2024
Closest in time.
A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, and R. Pedarsani, “Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization,” in Proc. Int. Con. Artif. Intell. Stat. , 2020, pp. 2021–2031
2031
Closest in time.