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  1. H Hellström, JMB da Silva Jr, MM Amiri, M Chen, V Fodor, HV Poor, C.Fischione, Wireless for Machine Learning: A Survey Foundations and Trends® in Signal Processing 15 (4), 290-39922022
  2. DP Souza, R Du, B da Silva Jr, J Mairton, CC Cavalcante, C Fischione, Leakage Detection In Water Distribution Networks: Efficient Training By Data Clustering, IWA World Water Congress & Exhibition 2022
  3. P Park, P Di Marco, C Fischione, Optimized over-the-air computation for wireless control systems, IEEE Communications Letters 26 (2), 2022
  4. H Hellström, V Fodor, C Fischione, Over-the-Air Federated Learning with Retransmissions, 2021 IEEE 22nd International Workshop on Signal Processing Advances in Communications
  5. JMB da Silva, K Ntougias, I Krikidis, G Fodor, C Fischione, Simultaneous Wireless Information and Power Transfer for Federated Learning, 2021 IEEE 22nd International Workshop on Signal Processing Advances in Communications
  6. P Park, P Di Marco, C Fischione, Wireless for control: Over-the-air controller, IEEE Communications Letters 25 (10), 3437-34411, 2021
  7. R Du, TO Timoudas, C Fischione, Comparing backscatter communication and energy harvesting in massive IoT networks, IEEE Transactions on Wireless Communications 21 (1), 429-4431, 2021
  8. P Park, HS Ghadikolaei, C Fischione, Proactive fault-tolerant wireless mesh networks for mission-critical control systems, Journal of Network and Computer Applications 186, 1030824, 2021
  9. Y Kim, E Al Hakim, J Haraldson, H Eriksson, JMB da Silva, C Fischione, Dynamic clustering in federated learning, ICC 2021-IEEE International Conference on Communications, 1-610, 2021
  10. R Du, S Magnusson, C Fischione, The Internet of Things as a deep neural network, IEEE Communications Magazine 58 (9), 20-2511, 2020
  11. R Du, S Magnússon, C Fischione, The IoT as a Deep Neural Network, IEEE Communications Magazine, 2, 2020
  12. TO Timoudas, R Du, C Fischione, Enabling massive IoT in ambient backscatter communication systems, ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-64 2020
  13. A Mahmoudi, HS Ghadikolaei, C Fischione, Cost-efficient distributed optimization in machine learning over wireless networks, ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-77, 2020
  14. A Mahmoudi, HS Ghadikolaei, C Fischione, Machine learning over networks: co-design of distributed optimization and communications, 2020 IEEE 21st International Workshop on Signal Processing Advances in Communications
  15. M. Zeng, V. Fodor, Energy minimization for delay constrained mobile edge computing with orthogonal and non-orthogonal multiple access, Ad Hoc Networks, Vol. 98, 2020.
  16. M. Zeng and V. Fodor, “Parallel Processing at the Edge in Dense Wireless Networks,” in IEEE Open Journal of the Communications Society, vol. 3, 2022
  17. J. A. Peris and V. Fodor, “Modelling multi-cell edge video analytics,” ICC 2022 – IEEE International Conference on Communications, 2022
  18. J. A. Peris and V. Fodor, “Distributed Join-the-Shortest-Queue with Sparse and Unreliable Information Updates,” ICC 2022 – IEEE International Conference on Communications, 2022
  19. J. A. Peris and V. Fodor, “Resource Dimensioning for Single-Cell Edge Video Analytics”, Swedish National Computer Networking Workshop, SNCNW, 2022.
  20. H. Hellström, V. Fodor and C. Fischione, “Over-the-Air Federated Learning with Retransmissions,” IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2021
  21. H. Hellström, V. Fodor, C. Fischione, “Unbiased Over-the-Air Computation via Retransmissions,” IEEE , Global Communications Conference (GLOBECOM), 2022.
  22. H. Hellström, V. Fodor, C. Fischione, “Bias Minimization for Over-the-Air Computation,” Swedish National Computer Networking Workshop, SNCNW, 2022.
  23. X. Wang, F. S. Samani, and R. Stadler, “Online feature selection for rapid, low-overhead learning in networked systems,” 16th International Conference on Network and Service Management (CNSM 2020). IEEE, 2020.
  24. R. S. Villaca and R. Stadler, “Online learning under resources constraints,” in 2021 17th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2021). IEEE, 2021.
  25. X. Wang, FS. Samani, A. Johnsson,R. Stadler, “Online Feature Selection for Low-overhead Learning in Networked Systems (Demonstration),” In 2021 17th International Conference on Network and Service Management (CNSM) 2021 Oct 25 (pp. 527-529). IEEE, 2021.
  26. X. Wang, R. Stadler, “Online Feature Selection for Efficient Learning in Networked System,” IEEE Transactions on Network and Service Management. 2022 Jun 8.
  27. L. Lindström, S. Gracy, S. Magnússon and H. Sandberg, “Leakage Localization in Water Distribution Networks: A Model-Based Approach”, European Control Conference, 2022
  28. A. Alanwar, A. Berndt, K.H. Johansson and H. Sandberg, “Data-Driven Set-Based Estimation Using Matrix Zonotopes with Set Containment Guarantees”, European Control Conference, 2022
  29. X. Wu, S. Magnússon, H. Reza Feyzmahdavian, M. Johansson, “Optimal convergence rates of totally asynchronous optimization”, IEEE Conference on Decision and Control (CDC), 2022
  30. M, Zhang, Q. Xu, S Magnússon, R. Pilawa-Podgurski and G. Guo, “Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System”, IEEE Energy Conversion Congress and Exposition, 2022
  31. X. Wu, S. Magnússon, H. Reza Feyzmahdavian, and M. Johansson, “Delay-adaptive step-sizes for asynchronous learning” International Conference on Machine Learning (ICML), 2022
  32. I. Haasler, A. Ringh, Y. Chen, J. Karlsson, “Multi-marginal optimal transport with a tree-structured cost and the Schroedinger bridge problem,” SIAM Journal on Control and Optimization, 59(4), 2428-2453, 2021.
  33. I. Haasler, J. Karlsson, and A. Ringh, “Control and estimation of ensembles via structured optimal transport, A computational approach based on entropy-regularized multi-marginal optimal transport,” IEEE Control Systems Magazine 41 (4), 50-69, 2021.
  34. I. Haasler, A. Ringh, Y. Chen, and J. Karlsson, “Efficient computations of multi-species mean field games via graph-structured optimal transport,” IEEE Conference on Decision and Control, 2021.
  35. J. Fan, I. Haasler, J. Karlsson, and Y. Chen, “On the complexity of the optimal transport problem with graph-structured cost,” AISTATS, 2022.
  36. B. Ahlgren, and K-J Grinnemo, “ZQTRTT: a multipath scheduler for heterogeneous traffic in ICNs based on zero queueing time ratio”, In Proceedings of the 9th ACM Conference on Information-Centric Networking (ICN ’22), 2022.