Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Open accessAbstract
Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles and, thereby, present communication-efficient and distributed learning frameworks with selected use cases.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
FedAvg makes the limited assumption that all IoT device clients join in each communication round for FL training.
“Indeed, the well-known FL algorithm FedAvg makes limited assumption that all IoT device clients join in each communication round for FL training [192]”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [192]
Section 3.1 (p. 3) describes FL workers periodically uploading model parameters and the server broadcasting a global model to all workers. It does not say that FedAvg assumes every IoT client participates in every communication round; searches for FedAvg and all-client participation found no passage establishing that assumption. (LLM, conf=0.73)
Experimental — accuracy not yet measuredSplit DL can be typically applied to medical diagnosis and millimeter wave channel prediction.
“It can be typically applied to the medical diagnosis and millimeter wave channel prediction [37].”
From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772 · see citing works
Self-citationFederated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322 · see citing works
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · see citing works
mixup: Beyond Empirical Risk Minimization
Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv · doi:10.48550/arxiv.1710.09412 · Cited by 5,831 · see citing works
Gaussian Processes in Machine Learning
Rasmussen, C. E. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28650-9_4 · Cited by 5,727 · see citing works
Channel Coding Rate in the Finite Blocklength Regime
Polyanskiy, Y., Poor, H. V., & Verdú, S. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2010.2043769 · Cited by 3,837 · see citing works
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766 · see citing works
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp MinimaPreprint
Keskar, N., Mudigere, D., Nocedal, J., Smelyanskiy, M., & Tang, P. T. P. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1609.04836 · Cited by 3,682 · see citing works
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works
Lasry, J., & Lions, P. (2007) · Japanese journal of mathematics · doi:10.1007/s11537-007-0657-8 · Cited by 2,639 · see citing works
An Introduction to Variational Autoencoders
Kingma, D. P., & Welling, M. (2019) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000056 · Cited by 2,609 · see citing works
On the Partial Difference Equations of Mathematical Physics
Courant, R., Friedrichs, K. O., & Lewy, H. (1967) · IBM Journal of Research and Development · doi:10.1147/rd.112.0215 · Cited by 2,513 · see citing works
Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334 · see citing works
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
Frid-Adar, M., Diamant, I., Klang, E., Amitai, M. M., Goldberger, J., & Greenspan, H. K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.013 · Cited by 1,952 · see citing works
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Nishio, T., & Yonetani, R. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761315 · Cited by 1,875 · see citing works
A Quantum Approximate Optimization Algorithm
Farhi, E., Goldstone, J., & Gutmann, S. (2014) · arXiv · doi:10.48550/arxiv.1411.4028 · Cited by 1,789 · see citing works
Exploiting Unintended Feature Leakage in Collaborative Learning
Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00029 · Cited by 1,734 · see citing works
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725 · see citing works
Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications
Khoshelham, K., & Elberink, S. O. (2012) · Sensors · doi:10.3390/s120201437 · Cited by 1,611 · see citing works
On the Convergence of FedAvg on Non-IID Data
Li, X., Huang, K., Yang, W., Wang, S., & Zhang, Z. (2019) · arXiv · doi:10.48550/arxiv.1907.02189 · Cited by 1,575 · see citing works
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Jacot, A., Gabriel, F., & Hongler, C. (2018) · arXiv · doi:10.48550/arxiv.1806.07572 · Cited by 1,562 · see citing works
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks
Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., & Cui, S. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3024629 · Cited by 1,547 · see citing works
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Zhang, K., Yang, Z., & Başar, T. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-60990-0_12 · Cited by 1,403 · see citing works
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Hitaj, B., Ateniese, G., & Perez-Cruz, F. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134012 · Cited by 1,377 · see citing works
On the Partial Difference Equations of Mathematical Physics
Courant, R., Friedrichs, K. O., & Lewy, H. (2002) · Birkhäuser Boston eBooks · doi:10.1007/978-1-4612-2080-0_6 · Cited by 1,353 · see citing works
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. A. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2926625 · Cited by 1,312 · see citing works
Self-citationFederated Multi-Task LearningPreprint
Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10467 · Cited by 1,282 · see citing works
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
Alistarh, D., Grubic, D., Li, J. Z., Tomioka, R., & Vojnović, M. (2016) · London School of Economics and Political Science Research Online (London School of Economics and Political Science) · doi:10.48550/arxiv.1610.02132 · Cited by 1,189 · see citing works
Toward Massive, Ultrareliable, and Low-Latency Wireless Communication With Short Packets
Durisi, G., Koch, T., & Popovski, P. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2537298 · Cited by 1,182 · see citing works
Energy Efficient Federated Learning Over Wireless Communication Networks
Yang, Z., Chen, M., Saad, W., Hong, C. S., & Shikh-Bahaei, M. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3037554 · Cited by 1,153 · see citing works
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122 · see citing works
Federated Learning via Over-the-Air Computation
Yang, K., Jiang, T., Shi, Y., & Ding, Z. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2961673 · Cited by 1,084 · cited 1× · see citing works
“, global model Θ = (1/N) N n=1 θn with N workers, rather than the individual updates θn, several recent works have utilized uncoded transmissions, so as to harness interference without separate channel allocation [62], [109]–[114].”
BackgroundOptimizing Federated Learning on Non-IID Data with Reinforcement Learning
Wang, H., Kaplan, Z., Niu, D., & Li, B. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155494 · Cited by 1,054 · see citing works
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800286 · Cited by 1,030 · cited 1× · see citing works
“Realizing the aforementioned pressing concern has recently sparked huge attention to the introduction of machine learning (ML) based approaches into communication system designs [11]–[14].”
BackgroundDual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
Duchi, J. C., Agarwal, A., & Wainwright, M. J. (2012) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2161027 · Cited by 947 · see citing works
Mixup: Beyond empirical risk minimizationPreprint
Zhang, H. (2024) · arXiv (Cornell University) · doi:10.57702/dcy1c3gw · Cited by 923 · see citing works
Manifold Mixup: Better Representations by Interpolating Hidden StatesPreprint
Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A. C., López-Paz, D., & Bengio, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05236 · Cited by 919 · see citing works
Zhou, L., Wang, S.-T., Choi, S., Pichler, H., & Lukin, M. D. (2020) · Physical Review X · doi:10.1103/physrevx.10.021067 · Cited by 913 · see citing works
Blockchained On-Device Federated Learning
Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2019.2921755 · Cited by 887 · see citing works
Self-citationDual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
Duchi, J. C., Agarwal, A., & Wainwright, M. J. (2010) · Cited by 884 · see citing works
Deep Learning Based Communication Over the Air
Dorner, S., Cammerer, S., Hoydis, J., & Brink, S. T. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2784180 · Cited by 859 · see citing works
Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L. (2017) · ACM SIGARCH Computer Architecture News · doi:10.1145/3093337.3037698 · Cited by 811 · see citing works
Mean Field Games and Mean Field Type Control Theory
Bensoussan, A., Frehse, J., & Yam, S. C. P. (2013) · SpringerBriefs in mathematics · doi:10.1007/978-1-4614-8508-7 · Cited by 789 · see citing works
Broadband Analog Aggregation for Low-Latency Federated Edge Learning
Zhu, G., Wang, Y., & Huang, K. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2946245 · Cited by 786 · see citing works
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep LearningPreprint
Wen, W., Xu, C., Yan, F., Wu, C., Wang, Y., Chen, Y., & Li, H. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07878 · Cited by 718 · see citing works
Yu, H., Yang, S., & Zhu, S.-H. (2018) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015693 · Cited by 696 · see citing works
An Introduction to Variational Autoencoders
Kingma, D. P., & Welling, M. (2019) · doi:10.1561/9781680836233 · Cited by 674 · see citing works
Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L. (2017) · Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3037697.3037698 · Cited by 673 · see citing works
Millimeter wave communication: a comprehensive survey
Wang, X., Kong, L., Kong, F., Qiu, F., Xia, M., Arnon, S., & Chen, G. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844322 · Cited by 656 · see citing works
Scheduling Policies for Federated Learning in Wireless Networks
Yang, H. H., Liu, Z., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2944169 · Cited by 640 · see citing works
Federated Learning Over Wireless Fading Channels
Amiri, M. M., & Gunduz, D. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2974748 · Cited by 625 · see citing works
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613 · see citing works
Revisiting Distributed Synchronous SGDPreprint
Pan, X., Chen, J., Monga, R., Samy Bengio, A., & Józefowicz, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05800 · Cited by 608 · see citing works
Model Pruning Enables Efficient Federated Learning on Edge Devices
Jiang, Y., Wang, S., Valls, V., Ko, B. J., Lee, W., Leung, K. K., & Tassiulas, L. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3166101 · Cited by 594 · see citing works
Optimal Distributed Online Prediction using Mini-BatchesPreprint
DekelOfer, Gilad-BachrachRan, ShamirOhad, & Xiaolin (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1012.1367 · Cited by 564 · see citing works
On coding for reliable communication over packet networks
Lun, D. S., Médard, M., Koetter, R., & Effros, M. (2008) · Physical Communication · doi:10.1016/j.phycom.2008.01.006 · Cited by 552 · see citing works
Federated Optimization:Distributed Optimization Beyond the DatacenterPreprint
Konečný, J., McMahan, B., & Ramage, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03575 · Cited by 547 · see citing works
signSGD: Compressed Optimisation for Non-Convex Problems
Bernstein, J., Wang, Y.-X., Azizzadenesheli, K., & Anandkumar, A. (2018) · arXiv · doi:10.48550/arxiv.1802.04434 · Cited by 541 · see citing works
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization
Nedić, A., Olshevsky, A., & Rabbat, M. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2817461 · Cited by 530 · see citing works
Split learning for health: Distributed deep learning without sharing raw patient data
Vepakomma, P., Gupta, O., Swedish, T., & Raskar, R. (2018) · arXiv · doi:10.48550/arxiv.1812.00564 · Cited by 513 · see citing works
FedML: A Research Library and Benchmark for Federated Machine LearningPreprint
He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13518 · Cited by 504 · see citing works
Communication-efficient edge ai: algorithms and systems
Shi, Y., Yang, K., Jiang, T., Zhang, J., & Letaief, K. B. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3007787 · Cited by 488 · see citing works
Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2018) · arXiv · doi:10.48550/arxiv.1811.11479 · Cited by 465 · see citing works
Self-citationDistributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
Samarakoon, S., Bennis, M., Saad, W., & Debbah, M. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2956472 · Cited by 459 · see citing works
Self-citationLocal Differential Privacy-Based Federated Learning for Internet of Things
Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3037194 · Cited by 456 · see citing works
Key drivers and research challenges for 6G ubiquitous wireless intelligence
Latva‐aho, M., Leppänen, K., Clazzer, F., & Munari, A. (2019) · elib (German Aerospace Center) · Cited by 454 · see citing works
Applied Federated Learning: Improving Google Keyboard Query SuggestionsPreprint
Yang, T. T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., & Beaufays, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02903 · Cited by 450 · see citing works
Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks
Popovski, P., Nielsen, J. J., Stefanović, Č., de Carvalho, E., Ström, E. G., Trillingsgaard, K. F., Bana, A.-S., Kim, D. M., Kotaba, R., Park, J., et al. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700258 · Cited by 437 · see citing works
Self-citationHierarchical Federated Learning ACROSS Heterogeneous Cellular Networks
Abad, M. S. H., Ozfatura, E., GUndUz, D., & Ercetin, O. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9054634 · Cited by 424 · see citing works
Towards Understanding Knowledge DistillationPreprint
Phuong, M., & Lampert, C. H. (2019) · International Conference on Machine Learning · doi:10.48550/arxiv.2105.13093 · Cited by 423 · see citing works
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301590 · Cited by 392 · see citing works
An Introduction to Analog and Digital Communications
Haykin, S. (1989) · Cited by 390 · see citing works
Girard, A., Rasmussen, C. E., Candela, J. Q., & Murray‐Smith, R. (2002) · Cited by 384 · see citing works
Zhu, G., Du, Y., Gunduz, D., & Huang, K. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3039309 · Cited by 376 · see citing works
GAN Augmentation: Augmenting Training Data using Generative Adversarial NetworksPreprint
Bowles, C. T., Chen, L., Guerrero, R., Bentley, P., Gunn, R. N., Hammers, A., Dickie, D. A., Hernández, M. D. C. V., Wardlaw, J. M., & Rueckert, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10863 · Cited by 347 · see citing works
Large scale distributed neural network training through online distillationPreprint
Anil, R., Pereyra, G., Passos, A., Ormándi, R., Dahl, G. E., & Hinton, G. E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.03235 · Cited by 315 · see citing works
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Broughton, M., Verdon, G., McCourt, T., Martinez, A. J., Yoo, J. H., Isakov, S. V., Massey, P., Halavati, R., Niu, M. Y., Zlokapa, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2003.02989 · Cited by 308 · see citing works
A Secure Federated Learning Framework for 5G Networks
Liu, Y., Peng, J., Kang, J., Iliyasu, A. M., Niyato, D., & El-Latif, A. A. A. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.01.1900525 · Cited by 290 · see citing works
UVeQFed: Universal Vector Quantization for Federated Learning
Shlezinger, N., Chen, M., Eldar, Y. C., Poor, H. V., & Cui, S. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3046971 · Cited by 275 · see citing works
Local differential privacy and its applications: A comprehensive survey
Yang, M., Guo, T., Zhu, T., Tjuawinata, I., Zhao, J., & Lam, K. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103827 · Cited by 270 · see citing works
Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games
Wang, X., & Sandholm, T. (2002) · Cited by 251 · see citing works
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed LearningPreprint
Chen, T., Giannakis, G. B., Sun, T., & Yin, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09965 · Cited by 242 · see citing works
Over-the-Air Computing for Wireless Data Aggregation in Massive IoT
Zhu, G., Xu, J., Huang, K., & Cui, S. R. (2021) · IEEE Wireless Communications · doi:10.1109/mwc.011.2000467 · Cited by 236 · see citing works
Privacy for Free: Wireless Federated Learning via Uncoded Transmission With Adaptive Power Control
Liu, D., & Simeone, O. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036948 · Cited by 234 · see citing works
On universal quantization by randomized uniform/lattice quantizers
Zamir, R., & Feder, M. (1992) · IEEE Transactions on Information Theory · doi:10.1109/18.119699 · Cited by 224 · see citing works
Threats to federated learning A survey
Lyu, L., Yu, H., & Yang, Q. (2020) · arXiv · doi:10.48550/arxiv.2003.02133 · Cited by 221 · see citing works
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data
Yoshida, N., Nishio, T., Morikura, M., Yamamoto, K., & Yonetani, R. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9149323 · Cited by 208 · see citing works
Towards Enabling Critical mMTC: A Review of URLLC Within mMTC
Pokhrel, S. R., Ding, J., Park, J., Park, O.-S., & Choi, J. (2020) · IEEE Access · doi:10.1109/access.2020.3010271 · Cited by 207 · see citing works
Self-citationAdaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Han, P., Wang, S., & Leung, K. K. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00026 · Cited by 191 · see citing works
Quality-Aware Streaming and Scheduling for Device-to-Device Video Delivery
Kim, J., Caire, G., & Molisch, A. F. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2015.2452272 · Cited by 188 · see citing works
Self-citationWireless Communications for Collaborative Federated Learning
Chen, M., Poor, H., Saad, W., & Cui, S. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.2000397 · Cited by 187 · see citing works
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural NetworksPreprint
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. A. (2017) · arXiv (Cornell University) · Cited by 184 · see citing works
Self-citationBetter Mini-Batch Algorithms via Accelerated Gradient Methods
Cotter, A., Shamir, O., Srebro, N., & Sridharan, K. (2011) · arXiv · doi:10.48550/arxiv.1106.4574 · Cited by 173 · see citing works
On Analog Gradient Descent Learning Over Multiple Access Fading Channels
Sery, T., & Cohen, K. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2989580 · Cited by 172 · see citing works
Error Compensated Quantized SGD and its Applications to Large-scale Distributed OptimizationPreprint
Wu, J., Huang, W., Huang, J., & Zhang, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08054 · Cited by 162 · see citing works
Tighter Theory for Local SGD on Identical and Heterogeneous Data
Khaled, A., Mishchenko, K., & Richtárik, P. (2019) · arXiv · doi:10.48550/arxiv.1909.04746 · Cited by 162 · see citing works
Detailed comparison of communication efficiency of split learning and federated learning
Singh, A., Vepakomma, P., Gupta, O., & Raskar, R. (2019) · arXiv · doi:10.48550/arxiv.1909.09145 · Cited by 148 · see citing works
Peer-to-peer Federated Learning on Graphs
Lalitha, A., Kilinc, O. C., Javidi, T., & Koushanfar, F. (2019) · arXiv · doi:10.48550/arxiv.1901.11173 · Cited by 146 · see citing works
A linear speedup analysis of distributed deep learning with sparse and quantized communication
Jiang, P., & Agrawal, G. (2018) · neural information processing systems · Cited by 142 · see citing works
Federated Learning with Compression: Unified Analysis and Sharp GuaranteesPreprint
Haddadpour, F., Kamani, M. M., Mokhtari, A., & Mahdavi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.01154 · Cited by 140 · see citing works
One-Shot Federated LearningPreprint
Guha, N., Talwalkar, A., & Smith, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11175 · Cited by 137 · see citing works
Machine Learning Meets Communication Networks: Current Trends and Future Challenges
Ahmad, I., Shahabuddin, S., Malik, H. H., Harjula, E., Leppänen, T., Lovén, L., Anttonen, A., Sodhro, A. H., Alam, M. M., Juntti, M. J., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3041765 · Cited by 135 · see citing works
Is Local SGD Better than Minibatch SGD?
Woodworth, B., Patel, K. K., Stich, S. U., Dai, Z., Bullins, B., McMahan, H. B., Shamir, O., & Srebro, N. (2020) · arXiv · doi:10.48550/arxiv.2002.07839 · Cited by 131 · see citing works
Hsu, W.-N., Zhang, Y., & Glass, J. (2017) · IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · doi:10.1109/asru.2017.8268911 · Cited by 124 · see citing works
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning
Zhang, H., Li, J. Z., Kara, K., Alistarh, D., Liu, J., & Zhang, C. (2017) · International Conference on Machine Learning · Cited by 124 · see citing works
Private Model Compression via Knowledge Distillation
Wang, J., Bao, W., Sun, L., Zhu, X., Cao, B., & Yu, P. S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011190 · Cited by 124 · see citing works
Machine Learning at the Edge: A Data-Driven Architecture With Applications to 5G Cellular Networks
Polese, M., Jana, R., Kounev, V., Zhang, K., Deb, S., & Zorzi, M. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.2999852 · Cited by 115 · see citing works
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data
Ahn, J.-H., Simeone, O., & Kang, J. (2019) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc.2019.8904164 · Cited by 114 · see citing works
A Review of Privacy-Preserving Federated Learning for the Internet-of-Things
Briggs, C., Fan, Z., & Andras, P. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-70604-3_2 · Cited by 101 · see citing works
Lee, J.-H., Park, J., Bennis, M., & Ko, Y. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3220696 · Cited by 100 · see citing works
Self-citationDistributed Learning with Compressed Gradient DifferencesPreprint
Mishchenko, K., Gorbunov, E., Takáč, M., & Richtárik, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09269 · Cited by 98 · see citing works
Extreme URLLC: Vision, Challenges, and Key EnablersPreprint
Park, J., Samarakoon, S., Shiri, H., Abdel-Aziz, M. K., Nishio, T., Elgabli, A., & Bennis, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.09683 · Cited by 98 · see citing works
Self-citationPark, J., Kim, S., & Zander, J. (2016) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2016.2540626 · Cited by 92 · see citing works
Self-citationNo Peek: A Survey of private distributed deep learningPreprint
Vepakomma, P., Swedish, T. B., Raskar, R., Gupta, O., & Dubey, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.03288 · Cited by 88 · see citing works
Multi-GPU Training of ConvNetsPreprint
Yadan, O., Adams, K., Taigman, Y., & Ranzato, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.5853 · Cited by 81 · see citing works
Kreps, D. M. (1989) · Game Theory · doi:10.1007/978-1-349-20181-5_19 · Cited by 80 · see citing works
Stochastic Distributed Learning with Gradient Quantization and Variance ReductionPreprint
Horváth, S., Kovalev, D. A., Mishchenko, K., Stich, S. U., & Richtárik, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05115 · Cited by 75 · see citing works
Convergence Time Minimization of Federated Learning over Wireless Networks
Chen, M., Poor, H. V., Saad, W., & Cui, S. R. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148815 · Cited by 75 · see citing works
HetMEC: Heterogeneous Multi-Layer Mobile Edge Computing in the 6 G Era
Zhang, Y., Di, B., Wang, P., Lin, J., & Song, L. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2975559 · Cited by 74 · see citing works
Acceleration for Compressed Gradient Descent in Distributed and Federated OptimizationPreprint
Li, Z., Kovalev, D. A., Qian, X., & Richtárik, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11364 · Cited by 72 · see citing works
Magnússon, S., Shokri‐Ghadikolaei, H., & Li, N. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3031073 · Cited by 71 · see citing works
Computation Scheduling for Distributed Machine Learning With Straggling Workers
Amiri, M. M., & Gündüz, D. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2952051 · Cited by 68 · see citing works
Resource-Efficient and Convergence-Preserving Online Participant Selection in Federated Learning
Jin, Y., Jiao, L., Qian, Z., Zhang, S., Lu, S., & Wang, X. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00049 · Cited by 68 · see citing works
Minibatch vs Local SGD for Heterogeneous Distributed LearningPreprint
Woodworth, B., Patel, K. K., & Srebro, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04735 · Cited by 65 · see citing works
Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction
Koda, Y., Park, J., Bennis, M., Yamamoto, K., Nishio, T., Morikura, M., & Nakashima, K. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.2978824 · Cited by 63 · see citing works
Self-citationXOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated LearningPreprint
Shin, M., Hwang, C., Kim, J., Park, J., Bennis, M., & Kim, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05148 · Cited by 61 · see citing works
Self-citationData augmentation and feature extraction using variational autoencoder for acoustic modeling
Nishizaki, H. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282225 · Cited by 58 · see citing works
Over-the-Air Machine Learning at the Wireless Edge
Amiri, M., & Gündüz, D. (2019) · International Workshop on Signal Processing Advances in Wireless Communications · doi:10.1109/spawc.2019.8815402 · Cited by 57 · see citing works
Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise
Koda, Y., Yamamoto, K., Nishio, T., & Morikura, M. (2020) · Global Communications Conference · doi:10.1109/globecom42002.2020.9322199 · Cited by 57 · see citing works
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
Lee, J.-H., Park, J., Bennis, M., & Ko, Y. (2020) · Globecom · doi:10.1109/globecom42002.2020.9348105 · Cited by 56 · see citing works
Self-citationQ-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Elgabli, A., Park, J., Bedi, A. S., Issaid, C. B., Bennis, M., & Aggarwal, V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3026398 · Cited by 55 · see citing works
Self-citationGADMM: Fast and Communication Efficient Framework for Distributed Machine LearningPreprint
Elgabli, A., Park, J., Bedi, A. S., Bennis, M., & Aggarwal, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00047 · Cited by 55 · see citing works
Self-citationComputation Scheduling for Distributed Machine Learning with Straggling Workers
Amiri, M. M., & Gündüz, D. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682911 · Cited by 50 · see citing works
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
Oh, S., Park, J., Jeong, E., Kim, H., Bennis, M., & Kim, S. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.3003693 · Cited by 49 · see citing works
Self-citationA Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!Preprint
Kovalev, D. A., Koloskova, A., Jaggi, M., Richtárik, P., & Stich, S. U. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.01697 · Cited by 47 · see citing works
Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach
Kim, H., Park, J., Bennis, M., & Kim, S. (2018) · IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2018.8445906 · Cited by 44 · see citing works
Self-citationQuantum Approximation for Wireless Scheduling
Choi, J., Oh, S., & Kim, J. (2020) · Applied Sciences · doi:10.3390/app10207116 · Cited by 44 · see citing works
Self-citationScheduling Policy and Power Allocation for Federated Learning in NOMA based MEC
Ma, X., Sun, H., & Hu, R. Q. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322270 · Cited by 44 · see citing works
Adaptive Task Allocation for Mobile Edge Learning
Mohammad, U., & Sorour, S. (2019) · IEEE Wireless Communications and Networking Conference Workshops (WCNCW) · doi:10.1109/wcncw.2019.8902527 · Cited by 37 · see citing works
Hybrid-FL: Cooperative Learning Mechanism Using Non-IID Data in Wireless Networks.Preprint
Yoshida, N., Nishio, T., Morikura, M., Yamamoto, K., & Yonetani, R. (2019) · arXiv (Cornell University) · Cited by 37 · see citing works
Local SGD: Unified Theory and New Efficient MethodsPreprint
Gorbunov, E., Hanzely, F., & Richtárik, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02828 · Cited by 37 · see citing works
FORESEEN: Towards Differentially Private Deep Inference for Intelligent Internet of Things
Lyu, L., Bezdek, J. C., Jin, J., & Yang, Y. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3000374 · Cited by 34 · see citing works
Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)Preprint
Zhu, G., Wang, Y., & Huang, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.11494 · Cited by 33 · see citing works
Cooperative Learning VIA Federated Distillation OVER Fading Channels
Ahn, J.-H., Simeone, O., & Kang, J. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053448 · Cited by 32 · see citing works
Towards Differentially Private Text Representations
Lyu, L., Li, Y., He, X., & Xiao, T. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401260 · Cited by 31 · see citing works
Communication-aware computing for edge processing
Li, S., Maddah-Ali, M. A., & Avestimehr, A. S. (2017) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2017.8007057 · Cited by 29 · see citing works
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Elgabli, A., Park, J., Bedi, A. S., Bennis, M., & Aggarwal, V. (2020) · Own your potential (DEAKIN) · Cited by 29 · see citing works
Self-citationDistilling On-Device Intelligence at the Network EdgePreprint
Park, J., Wang, S., Elgabli, A., Oh, S., Jeong, E., Cha, H., Kim, H., Kim, S., & Bennis, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05895 · Cited by 28 · see citing works
Self-citationShmCaffe: A Distributed Deep Learning Platform with Shared Memory Buffer for HPC Architecture
Ahn, S., Kim, J., Lim, E., Choi, W., Mohaisen, A., & Kang, S. (2018) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00111 · Cited by 26 · see citing works
Self-citationQ-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Elgabli, A., Park, J., Bedi, A. S., Bennis, M., & Aggarwal, V. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054491 · Cited by 26 · see citing works
Self-citationProxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
Cha, H., Park, J., Kim, H., Bennis, M., & Kim, S. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2994942 · Cited by 26 · see citing works
Self-citationUltra-dense edge caching under spatio-temporal demand and network dynamics
Kim, H., Park, J., Bennis, M., Kim, S., & Debbah, M. A. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7996764 · Cited by 25 · see citing works
Self-citationPrivacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms
Jeon, J., Kim, J., Kim, J., Kim, K., Mohaisen, A., & Kim, J. (2019) · IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S) · doi:10.1109/dsn-s.2019.00007 · Cited by 23 · see citing works
Self-citationScalable Centralized Deep Multi-Agent Reinforcement Learning via Policy GradientsPreprint
Khan, A., Zhang, C., Lee, D. D., Kumar, V., & Ribeiro, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08776 · Cited by 21 · see citing works
Magnússon, S., Shokri‐Ghadikolaei, H., & Li, N. (2019) · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/ieeeconf44664.2019.9049052 · Cited by 21 · see citing works
Multi-hop Federated Private Data Augmentation with Sample CompressionPreprint
Jeong, E., Oh, S., Park, J., Kim, H., Bennis, M., & Kim, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06426 · Cited by 20 · see citing works
Self-citationEnergy‐efficient and delay‐aware multitask offloading for mobile edge computing networks
Chanyour, T., Ghmary, M. E., Hmimz, Y., & Malki, M. O. C. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3673 · Cited by 17 · see citing works
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID DataPreprint
Yoshida, N., Nishio, T., Morikura, M., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07210 · Cited by 17 · see citing works
How Much of Wireless Rates Can Smartphones Support in 5G Networks?
Yang, J., Ge, X., & Zhong, Y. F. (2018) · IEEE Network · doi:10.1109/mnet.2018.1800025 · Cited by 15 · see citing works
One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction
Koda, Y., Park, J., Bennis, M., Yamamoto, K., Nishio, T., & Morikura, M. (2019) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/3360468.3368176 · Cited by 15 · see citing works
Self-citationOne pixel image and RF signal based split learning for mmwave received power prediction
Koda, Y., (7725152), K. Y., Park, J., (13363011), T. N., (13307190), M. B., & Morikura, M. (2019) · Figshare · Cited by 15 · see citing works
Self-citationFederated Reinforcement Distillation with Proxy Experience MemoryPreprint
Cha, H., Park, J., Kim, H., Kim, S., & Bennis, M. (2020) · doi:10.36227/techrxiv.12645497 · Cited by 15 · see citing works
Self-citationQueryGuard: Privacy-Preserving Latency-Aware Query Optimization for Edge Computing
Xu, R., Palanisamy, B., & Joshi, J. B. D. (2018) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom/bigdatase.2018.00153 · Cited by 14 · see citing works
Differentially Private Knowledge Distillation for Mobile Analytics
Lyu, L., & Chen, C.-H. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401259 · Cited by 14 · see citing works
Harnessing wireless channels for scalable and privacy-preserving federated learning
Elgabli, A., Park, J., Issaid, C. B., & Bennis, M. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3078783 · Cited by 14 · see citing works
Self-citationCommunication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMMPreprint
Issaid, C. B., Elgabli, A., Park, J., Bennis, M., & Debbah, M. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06459 · Cited by 11 · see citing works
Self-citationSmart scheduling and feedback allocation over non-stationary wireless channels
Karaca, M., Alpcan, T., & Erçetin, Ö. (2012) · International Conference on Communications · doi:10.1109/icc.2012.6364878 · Cited by 10 · see citing works
Shiri, H., Park, J., & Bennis, M. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3017281 · Cited by 8 · see citing works
Self-citationIntegrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial NetworksPreprint
Lee, J.-H., Park, J., Bennis, M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.12521 · Cited by 8 · see citing works
Self-citationScheduling Policy and Power Allocation for Federated Learning in NOMA Based MECPreprint
Ma, X., Sun, H., & Hu, R. Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.13044 · Cited by 8 · see citing works
Federated Reinforcement Distillation with Proxy Experience MemoryPreprint
Cha, H., Park, J., Kim, H., Kim, S., & Bennis, M. (2020) · doi:10.36227/techrxiv.12645497.v1 · Cited by 7 · see citing works
Self-citationDistributed Heteromodal Split Learning for Vision Aided mmWave Received Power PredictionPreprint
Koda, Y., Park, J., Bennis, M., Yamamoto, K., Nishio, T., & Morikura, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08208 · Cited by 5 · see citing works
Self-citationOn the Unreasonable Effectiveness of Knowledge Distillation: Analysis in the Kernel RegimePreprint
Rahbar, A., Panahi, A., Bhattacharyya, C., Dubhashi, D., & Chehreghani, M. H. (2020) · arXiv (Cornell University) · Cited by 5 · see citing works
Flagged first, then by citations
11 of the 192 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889 · see citing works
Federated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661 · see citing works
Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions
Getu, T. M., Kaddoum, G., & Bennis, M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3520707 · Cited by 110 · see citing works
Self-citationQ-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Elgabli, A., Park, J., Bedi, A. S., Issaid, C. B., Bennis, M., & Aggarwal, V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3026398 · Cited by 55 · see citing works
Self-citationMobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches
Zhong, R., Liu, X., Liu, Y., Chen, Y., & Han, Z. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3181747 · Cited by 47 · see citing works
Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired ConsensusPreprint
Chen, H., Asif, S. A., Park, J., Shen, C.-C., & Bennis, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03300 · Cited by 54 · see citing works
Self-citationNdikumana, A., Nguyen, K. K., & Cheriet, M. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3245544 · Cited by 27 · see citing works
Dynamic Quantum Federated Learning for UAV-Based Autonomous Surveillance
Park, S., Son, S. B., Jung, S., & Kim, J. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3526809 · Cited by 21 · see citing works
Self-citationHyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks
Rosato, A., Panella, M., & Kleyko, D. V. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533805 · Cited by 16 · see citing works
Distributed Generative Adversarial Networks for mmWave Channel Modeling in Wireless UAV Networks
Zhang, Q., Ferdowsi, A., & Saad, W. (2021) · International Conference on Communications · doi:10.1109/icc42927.2021.9501056 · Cited by 17 · see citing works
Harnessing wireless channels for scalable and privacy-preserving federated learning
Elgabli, A., Park, J., Issaid, C. B., & Bennis, M. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3078783 · Cited by 14 · see citing works
Self-citationParallelized and randomized adversarial imitation learning for safety-critical self-driving vehicles
Yun, W. J., Shin, M., Jung, S., Kwon, S.-C., & Kim, J. (2022) · Journal of Communications and Networks · doi:10.23919/jcn.2022.000012 · Cited by 12 · see citing works
Self-citationYun, W. J., Yi, S., & Kim, J. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9658625 · Cited by 12 · see citing works
Self-citationHeterogeneous networks: Fair power allocation in LTE-A uplink scenarios
Kurda, R. (2021) · PLoS ONE · doi:10.1371/journal.pone.0252421 · Cited by 11 · see citing works
Fair and Efficient Distributed Edge Learning With Hybrid Multipath TCP
Pokhrel, S. R., Choi, J., & Walid, A. (2022) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2022.3219924 · Cited by 11 · see citing works
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMMPreprint
Issaid, C. B., Elgabli, A., Park, J., Bennis, M., & Debbah, M. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06459 · Cited by 11 · see citing works
Self-citationRobustness and Diversity Seeking Data-Free Knowledge Distillation
Han, P., Park, J., Wang, S., & Liu, Y. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414674 · Cited by 9 · see citing works
Self-citationMishra, K. N., Bhattacharjee, V., Saket, S., & Mishra, S. P. (2022) · Research Square · doi:10.21203/rs.3.rs-1714576/v1 · Cited by 5 · see citing works
Leveraging User-Diversity in Energy-Efficient Edge-Facilitated Collaborative Fog Computing
Paris, A., Mirghasemi, H., Stupia, I., & Vandendorpe, L. (2021) · IEEE Access · doi:10.1109/access.2021.3094888 · Cited by 4 · see citing works
Age-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach
Hsu, Y.-L., Liu, C., Samarakoon, S., Wei, H., & Bennis, M. (2021) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc50174.2021.9569536 · Cited by 4 · see citing works
Self-citationMadhavi., S., & Karthikeyan, A. (2025) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs63635.2025.11031753 · Cited by 1 · see citing works
Zhou, Q. (2021) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca51532.2021.9544809 · Cited by 1 · see citing works
Emergence of Federated and Deep Learning for Smart City
Shashi, S., & Rana, V. K. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-2373-1.ch002
Развитие цифровых коммуникационных технологий в современном мире
Зибров, П. Ю. (2024) · ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ · doi:10.18411/trnio-01-2024-690
- Authors
- Park, J., Samarakoon, S., Elgabli, A., Kim, J., Bennis, M., Kim, S.-L., & Debbah, M.
- Affiliations
- Deakin University (AU)
- University of Oulu (FI)
- Korea University (KR)
- Yonsei University (KR)
- Centre National de la Recherche Scientifique (FR)
- ORCID
- Venue
- Proceedings of the IEEE
- Published
- 2021-05
- Volume
- 109
- Issue
- 5
- Pages
- 796-819
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W3047067813
- Cited by
- 25
- References
- 192
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 2 · 2022: 4 · 2021: 12 · 2020: 2
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the IEEE (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- INFOTECH Project NOOR
- NEGEIN Project
- EU-CHISTERA projects LeadingEdge and CONNECT
- EU-H2020 Project
- Academy of Finland projects MISSION and SMARTER
- Institute for Information & Communications Technology Planning & Evaluation
- Korea Government (MSIT) (2018-0-00170)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
- Keywords
- distributed learning, communication efficiency, wireless networks, edge computing, 5G, communication delay, model updating, transmission scheduling, time-varying channels
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
