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% Bibliography file for BibTeX + IEEEtran.bst
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% Useful entry types:
% article - journal paper
% inproceedings - conference paper
% book - book
% misc - website, software, etc.
% phdthesis - PhD thesis
% mastersthesis - Master thesis
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% Cite in .tex: \cite{key} or \cite{key1, key2}
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@inproceedings{10078005,
author = {Liu, Ye and Chang, Shan and Liu, Yiqi},
booktitle = {2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS)},
title = {FedCS: Communication-Efficient Federated Learning with Compressive Sensing},
year = {2023},
volume = {},
number = {},
pages = {17-24},
keywords = {Training;Adaptation models;Image coding;Costs;Federated learning;Servers;Task analysis;federated learning;communication-efficient;compressive sensing;dictionary learning},
doi = {10.1109/ICPADS56603.2022.00011}
}
@article{2024ITWC...23.7362C,
author = {{Cui}, Yiming and {Guo}, Jiajia and {Wen}, Chao-Kai and {Jin}, Shi},
title = {{Communication-Efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback}},
journal = {IEEE Transactions on Wireless Communications},
keywords = {Massive MIMO, CSI feedback, federated edge learning, neural network quantization, personalization, Electrical Engineering and Systems Science - Signal Processing},
year = 2024,
month = jan,
volume = {23},
number = {7},
pages = {7362-7375},
doi = {10.1109/TWC.2023.3339824},
archiveprefix = {arXiv},
eprint = {2303.13782},
primaryclass = {eess.SP},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024ITWC...23.7362C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{10050151,
author = {Jiang, Zhida and Xu, Yang and Xu, Hongli and Wang, Zhiyuan and Liu, Jianchun and Chen, Qian and Qiao, Chunming},
journal = {IEEE Transactions on Mobile Computing},
title = {Computation and Communication Efficient Federated Learning With Adaptive Model Pruning},
year = {2024},
volume = {23},
number = {3},
pages = {2003-2021},
keywords = {Computational modeling;Adaptation models;Training;Edge computing;Mobile computing;Bandwidth;Federated learning;Adaptive model pruning;edge computing;federated learning;heterogeneity},
doi = {10.1109/TMC.2023.3247798}
}
@article{10.3389/frwa.2024.1378598,
author = {El Hafyani, Mounia and El Himdi, Khalid and El Adlouni, Salah-Eddine },
title = {Improving monthly precipitation prediction accuracy using machine learning models: a multi-view stacking learning technique},
journal = {Frontiers in Water},
volume = {Volume 6 - 2024},
year = {2024},
url = {https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2024.1378598},
doi = {10.3389/frwa.2024.1378598},
issn = {2624-9375}
}
@article{2026ITWC...25.1981L,
author = {{Liang}, Yipeng and {Chen}, Qimei and {Li}, Rongpeng and {Zhu}, Guangxu and {Kaleem Awan}, Muhammad and {Jiang}, Hao},
title = {{Communication-and-Computation Efficient Split Federated Learning in Wireless Networks: Gradient Aggregation and Resource Management}},
journal = {IEEE Transactions on Wireless Communications},
keywords = {Communication-and-computation efficient, distributed training, edge AI, federated split learning, resource allocation, Computer Science - Distributed, Parallel, and Cluster Computing},
year = 2026,
month = jan,
volume = {25},
pages = {1981-1995},
doi = {10.1109/TWC.2025.3594006},
archiveprefix = {arXiv},
eprint = {2501.01078},
primaryclass = {cs.DC},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026ITWC...25.1981L},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{10522472,
author = {Zhu, Guangyu and Deng, Yiqin and Chen, Xianhao and Zhang, Haixia and Fang, Yuguang and Wong, Tan F.},
journal = {IEEE Internet of Things Journal},
title = {ESFL: Efficient Split Federated Learning Over Resource-Constrained Heterogeneous Wireless Devices},
year = {2024},
volume = {11},
number = {16},
pages = {27153-27166},
keywords = {Training;Computational modeling;Servers;Federated learning;Resource management;Data models;Load modeling;Distributed machine learning (ML);federated learning (FL);split learning;wireless networking},
doi = {10.1109/JIOT.2024.3397677}
}
@article{CHAN2023100375,
title = {New extreme rainfall projections for improved climate resilience of urban drainage systems},
journal = {Climate Services},
volume = {30},
pages = {100375},
year = {2023},
issn = {2405-8807},
doi = {https://doi.org/10.1016/j.cliser.2023.100375},
url = {https://www.sciencedirect.com/science/article/pii/S2405880723000365},
author = {Steven C. Chan and Elizabeth J. Kendon and Hayley J. Fowler and Benjamin D. Youngman and Murray Dale and Christopher Short}
}
@article{10478872,
author = {Mahmoudi, Afsaneh and Ghadikolaei, Hossein S. and Barros Da Silva, José Mairton and Fischione, Carlo},
journal = {IEEE Transactions on Wireless Communications},
title = {FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning},
year = {2024},
volume = {23},
number = {9},
pages = {11076-11093},
keywords = {Costs;Training;Wireless networks;Protocols;Optimization;Machine learning algorithms;Resource management;Federated learning;communication protocols;cost-efficient algorithm;latency;unfolding federated learning},
doi = {10.1109/TWC.2024.3378351}
}
@inproceedings{mu2023communication,
title = {Communication and storage efficient federated split learning},
author = {Mu, Yujia and Shen, Cong},
booktitle = {ICC 2023-IEEE International Conference on Communications},
pages = {2976--2981},
year = {2023},
organization = {IEEE}
}
@inproceedings{Chen_2021,
title = {Communication and Computation Reduction for Split Learning using Asynchronous Training},
url = {http://dx.doi.org/10.1109/SiPS52927.2021.00022},
doi = {10.1109/sips52927.2021.00022},
booktitle = {2021 IEEE Workshop on Signal Processing Systems (SiPS)},
author = {Chen, Xing and Li, Jingtao and Chakrabarti, Chaitali},
year = {2021},
month = oct,
pages = {76--81}
}
@article{GUBBI20131645,
title = {Internet of Things (IoT): A vision, architectural elements, and future directions},
journal = {Future Generation Computer Systems},
volume = {29},
number = {7},
pages = {1645-1660},
year = {2013},
note = {Including Special sections: Cyber-enabled Distributed Computing for Ubiquitous Cloud and Network Services \& Cloud Computing and Scientific Applications — Big Data, Scalable Analytics, and Beyond},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2013.01.010},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X13000241},
author = {Jayavardhana Gubbi and Rajkumar Buyya and Slaven Marusic and Marimuthu Palaniswami},
keywords = {Internet of Things, Ubiquitous sensing, Cloud computing, Wireless sensor networks, RFID, Smart environments},
abstract = {Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating–actuating network creates the Internet of Things (IoT), wherein sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fueled by the recent adaptation of a variety of enabling wireless technologies such as RFID tags and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a Cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A Cloud implementation using Aneka, which is based on interaction of private and public Clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.}
}
@article{6740844,
author = {Zanella, Andrea and Bui, Nicola and Castellani, Angelo and Vangelista, Lorenzo and Zorzi, Michele},
journal = {IEEE Internet of Things Journal},
title = {Internet of Things for Smart Cities},
year = {2014},
volume = {1},
number = {1},
pages = {22-32},
keywords = {Urban areas;Smart buildings;Monitoring;Smart homes;Business;IEEE 802.15 Standards;Constrained Application Protocol (CoAP);Efficient XML Interchange (EXI);network architecture;sensor system integration;service functions and management;Smart Cities;testbed and trials;6lowPAN},
doi = {10.1109/JIOT.2014.2306328}
}
@inproceedings{pmlr-v54-mcmahan17a,
title = {{Communication-Efficient Learning of Deep Networks from Decentralized Data}},
author = {McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and Arcas, Blaise Aguera y},
booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
pages = {1273--1282},
year = {2017},
editor = {Singh, Aarti and Zhu, Jerry},
volume = {54},
series = {Proceedings of Machine Learning Research},
month = {20--22 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf},
url = {https://proceedings.mlr.press/v54/mcmahan17a.html},
abstract = {Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent. }
}
@article{2019arXiv191204977K,
author = {{Kairouz}, Peter and {McMahan}, H. Brendan and {Avent}, Brendan and {Bellet}, Aur{\'e}lien and {Bennis}, Mehdi and {Nitin Bhagoji}, Arjun and {Bonawitz}, Kallista and {Charles}, Zachary and {Cormode}, Graham and {Cummings}, Rachel and {D'Oliveira}, Rafael G.~L. and {Eichner}, Hubert and {El Rouayheb}, Salim and {Evans}, David and {Gardner}, Josh and {Garrett}, Zachary and {Gasc{\'o}n}, Adri{\`a} and {Ghazi}, Badih and {Gibbons}, Phillip B. and {Gruteser}, Marco and {Harchaoui}, Zaid and {He}, Chaoyang and {He}, Lie and {Huo}, Zhouyuan and {Hutchinson}, Ben and {Hsu}, Justin and {Jaggi}, Martin and {Javidi}, Tara and {Joshi}, Gauri and {Khodak}, Mikhail and {Kone{\v{c}}n{\'y}}, Jakub and {Korolova}, Aleksandra and {Koushanfar}, Farinaz and {Koyejo}, Sanmi and {Lepoint}, Tancr{\`e}de and {Liu}, Yang and {Mittal}, Prateek and {Mohri}, Mehryar and {Nock}, Richard and {{\"O}zg{\"u}r}, Ayfer and {Pagh}, Rasmus and {Raykova}, Mariana and {Qi}, Hang and {Ramage}, Daniel and {Raskar}, Ramesh and {Song}, Dawn and {Song}, Weikang and {Stich}, Sebastian U. and {Sun}, Ziteng and {Theertha Suresh}, Ananda and {Tram{\`e}r}, Florian and {Vepakomma}, Praneeth and {Wang}, Jianyu and {Xiong}, Li and {Xu}, Zheng and {Yang}, Qiang and {Yu}, Felix X. and {Yu}, Han and {Zhao}, Sen},
title = {{Advances and Open Problems in Federated Learning}},
journal = {arXiv e-prints},
keywords = {Computer Science - Machine Learning, Computer Science - Cryptography and Security, Statistics - Machine Learning},
year = 2019,
month = dec,
eid = {arXiv:1912.04977},
pages = {arXiv:1912.04977},
doi = {10.48550/arXiv.1912.04977},
archiveprefix = {arXiv},
eprint = {1912.04977},
primaryclass = {cs.LG},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv191204977K},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{10279494,
author = {Papageorgiou, Yiannis and Karaliopoulos, Merkouris and Koutsopoulos, Iordanis},
booktitle = {ICC 2023 - IEEE International Conference on Communications},
title = {Controller Placement and TDMA Link Scheduling in Software Defined Wireless Multihop Networks},
year = {2023},
volume = {},
number = {},
pages = {640-646},
keywords = {Wireless communication;Time division multiple access;Wireless sensor networks;Software algorithms;Spread spectrum communication;Quality of service;Software;Controller placement problem;Time division multiple access(TDMA);Cross-layer optimization;Software Defined Networks},
doi = {10.1109/ICC45041.2023.10279494}
}
@misc{vepakomma2018splitlearninghealthdistributed,
title = {Split learning for health: Distributed deep learning without sharing raw patient data},
author = {Praneeth Vepakomma and Otkrist Gupta and Tristan Swedish and Ramesh Raskar},
year = {2018},
eprint = {1812.00564},
archiveprefix = {arXiv},
primaryclass = {cs.LG},
url = {https://arxiv.org/abs/1812.00564}
}
@article{hersbach2020era5,
title = {The {ERA5} global reanalysis},
author = {Hersbach, Hans and Bell, Bill and Berrisford, Paul and others},
journal = {Quarterly Journal of the Royal Meteorological Society},
volume = {146},
number = {730},
pages = {1999--2049},
year = {2020}
}
@article{rasp2020weatherbench,
title = {{WeatherBench}: A benchmark data set for data-driven weather forecasting},
author = {Rasp, Stephan and Dueben, Peter D and others},
journal = {Journal of Advances in Modeling Earth Systems},
volume = {12},
number = {11},
year = {2020}
}
@article{saeed2021rainfall,
title = {Rainfall prediction using machine learning techniques: A systematic review},
author = {Saeed, Saima and others},
journal = {IEEE Access},
volume = {9},
pages = {141353--141371},
year = {2021}
}
@book{wmo2018guide,
author = {{World Meteorological Organization}},
title = {Guide to Instruments and Methods of Observation: Volume I – Measurement of Meteorological Variables},
publisher = {WMO-No. 8},
year = {2018},
address = {Geneva, Switzerland}
}
@article{9026781,
author = {Koda, Yusuke and Park, Jihong and Bennis, Mehdi and Yamamoto, Koji and Nishio, Takayuki and Morikura, Masahiro and Nakashima, Kota},
journal = {IEEE Communications Letters},
title = {Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction},
year = {2020},
volume = {24},
number = {6},
pages = {1284-1288},
doi = {10.1109/LCOMM.2020.2978824}
}
@Article{network4010001,
AUTHOR = {Almutairi, Hanin and Zhang, Ning},
TITLE = {A Survey on Routing Solutions for Low-Power and Lossy Networks: Toward a Reliable Path-Finding Approach},
JOURNAL = {Network},
VOLUME = {4},
YEAR = {2024},
NUMBER = {1},
PAGES = {1--32},
URL = {https://www.mdpi.com/2673-8732/4/1/1},
ISSN = {2673-8732},
ABSTRACT = {Low-Power and Lossy Networks (LLNs) have grown rapidly in recent years owing to the increased adoption of Internet of Things (IoT) and Machine-to-Machine (M2M) applications across various industries, including smart homes, industrial automation, healthcare, and smart cities. Owing to the characteristics of LLNs, such as Lossy channels and limited power, generic routing solutions designed for non-LLNs may not be adequate in terms of delivery reliability and routing efficiency. Consequently, a routing protocol for LLNs (RPL) was designed. Several RPL objective functions have been proposed to enhance the routing reliability in LLNs. This paper analyses these solutions against performance and security requirements to identify their limitations. Firstly, it discusses the characteristics and security issues of LLN and their impact on packet delivery reliability and routing efficiency. Secondly, it provides a comprehensive analysis of routing solutions and identifies existing limitations. Thirdly, based on these limitations, this paper highlights the need for a reliable and efficient path-finding solution for LLNs.},
DOI = {10.3390/network4010001}
}
@article{MARTINBAOS2022108282,
title = {IoT based monitoring of air quality and traffic using regression analysis},
journal = {Applied Soft Computing},
volume = {115},
pages = {108282},
year = {2022},
issn = {1568-4946},
doi = {https://doi.org/10.1016/j.asoc.2021.108282},
url = {https://www.sciencedirect.com/science/article/pii/S1568494621010917},
author = {José Ángel Martín-Baos and Luis Rodriguez-Benitez and Ricardo García-Ródenas and Jun Liu},
keywords = {Air Quality Index, Regression modelling, Video motion vectors, Embedded systems},
abstract = {Dynamic traffic management (DTM) systems are used to reduce the negative externalities of traffic congestion, such as air pollution in urban areas. They require traffic and environmental monitoring infrastructures. In this paper we present a prototype of a low-cost Internet of Things (IoT) system for monitoring traffic flow and the Air Quality Index (AQI). The computation of the traffic flows is based on processing video in the compressed domain. Only using motion vectors as input, traffic flow is computed in real-time over an embedded architecture. An estimation of the AQI is supported by machine learning regression techniques, using different feature data obtained from the IoT device. These automatic learning techniques overcome the need for complex calibration and other limitations of embedded devices in making the needed measurements of the pollutant gases for the computation of the actual AQI. The experimentation with the data obtained from different cities representing different scenarios with a variety of climate and traffic conditions, allows validating the proposed architecture. As regressors, Linear Regression (LR), Gaussian Process Regression (GPR) and Random Forest (RF) are compared using the performance metrics R2, MSE, MAE and MRE resulting in a relevant improvement of the AQI estimations of our proposal.}
}
@article{di2025federated,
title={Federated Learning for Distributed Weather Forecasting: A Practical Approach on Real Multidimensional Georeferenced Data},
author={Di Vicino, Attilio and Fiorillo, Giuseppe and Galluccio, Luigi and Montella, Raffaele},
year={2025}
}
@article{zheng2023reducing,
title={Reducing communication for split learning by randomized top-k sparsification},
author={Zheng, Fei and Chen, Chaochao and Lyu, Lingjuan and Yao, Binhui},
journal={arXiv preprint arXiv:2305.18469},
year={2023}
}
@misc{thapa2022splitfedfederatedlearningmeets,
title={SplitFed: When Federated Learning Meets Split Learning},
author={Chandra Thapa and M. A. P. Chamikara and Seyit Camtepe and Lichao Sun},
year={2022},
eprint={2004.12088},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2004.12088},
}
@misc{shiranthika2024splitfedziplearnedcompressiondata,
title={SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning},
author={Chamani Shiranthika and Hadi Hadizadeh and Parvaneh Saeedi and Ivan V. Bajić},
year={2024},
eprint={2412.17150},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2412.17150},
}
@ARTICLE{10791300,
author={Zhang, Junhe and Ni, Wanli and Wang, Dongyu},
journal={IEEE Transactions on Vehicular Technology},
title={Federated Split Learning With Model Pruning and Gradient Quantization in Wireless Networks},
year={2025},
volume={74},
number={4},
pages={6850-6855},
keywords={Training;Computational modeling;Servers;Convergence;Wireless networks;Quantization (signal);Load modeling;Vectors;Propagation losses;Data models;Federated split learning;model pruning;gradient quantization;dropout;convergence analysis},
doi={10.1109/TVT.2024.3515083}}
@ARTICLE{11458714,
author={Lin, Zehang and Lin, Zheng and Yang, Miao and Huang, Jianhao and Zhang, Yuxin and Fang, Zihan and Du, Xia and Chen, Zhe and Zhu, Shunzhi and Ni, Wei},
journal={IEEE Transactions on Vehicular Technology},
title={SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression},
year={2026},
volume={},
number={},
pages={1-6},
keywords={Low earth orbit satellites;Artificial satellites;Communication systems;Internet;Internet of Things;Electronic mail;Product development;Data communication;Semantic communication;Vehicular ad hoc networks;Distributed learning;edge intelligence;split learning},
doi={10.1109/TVT.2026.3679518}}
@misc{lin2026slfaccommunicationefficientsplitlearning,
title={SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression},
author={Zehang Lin and Miao Yang and Haihan Zhu and Zheng Lin and Jianhao Huang and Jing Yang and Guangjin Pan and Dianxin Luan and Zihan Fang and Shunzhi Zhu and Wei Ni and John Thompson},
year={2026},
eprint={2604.07316},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2604.07316},
}
@misc{lin2025slacccommunicationefficientsplitlearning,
title={SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression},
author={Zehang Lin and Zheng Lin and Miao Yang and Jianhao Huang and Yuxin Zhang and Zihan Fang and Xia Du and Zhe Chen and Shunzhi Zhu and Wei Ni},
year={2025},
eprint={2508.12984},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.12984},
}
@misc{liang2025communicationandcomputationefficientsplitfederated,
title={Communication-and-Computation Efficient Split Federated Learning: Gradient Aggregation and Resource Management},
author={Yipeng Liang and Qimei Chen and Guangxu Zhu and Muhammad Kaleem Awan and Hao Jiang},
year={2025},
eprint={2501.01078},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2501.01078},
}
@misc{li2026splitcomcommunicationefficientsplitfederated,
title={SplitCom: Communication-efficient Split Federated Fine-tuning of LLMs via Temporal Compression},
author={Tao Li and Yulin Tang and Yiyang Song and Cong Wu and Xihui Liu and Pan Li and Xianhao Chen},
year={2026},
eprint={2602.10564},
archivePrefix={arXiv},
primaryClass={cs.NI},
url={https://arxiv.org/abs/2602.10564},
}