@inproceedings{fedavg,
  title={Communication-efficient learning of deep networks from decentralized data},
  author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera},
  booktitle={Artificial intelligence and statistics},
  pages={1273--1282},
  year={2017},
  organization={PMLR}
}

@article{xu2023asynchronous,
  title={Asynchronous federated learning on heterogeneous devices: A survey},
  author={Xu, Chenhao and Qu, Youyang and Xiang, Yong and Gao, Longxiang},
  journal={Computer Science Review},
  volume={50},
  pages={100595},
  year={2023},
  publisher={Elsevier}
}
@article{wang2022asynchronous,
  title={Asynchronous federated learning over wireless communication networks},
  author={Wang, Zhongyu and Zhang, Zhaoyang and Tian, Yuqing and Yang, Qianqian and Shan, Hangguan and Wang, Wei and Quek, Tony QS},
  journal={IEEE Transactions on Wireless Communications},
  volume={21},
  number={9},
  pages={6961--6978},
  year={2022},
  publisher={IEEE}
}



@inproceedings{yang2021achieving,
  title={Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning},
  author={Yang, Haibo and Fang, Minghong and Liu, Jia},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

@article{kairouz2021advances,
  title={Advances and open problems in federated learning},
  author={Kairouz, Peter and McMahan, H Brendan and Avent, Brendan and Bellet, Aur{\'e}lien and Bennis, Mehdi and Bhagoji, Arjun Nitin and Bonawitz, Kallista and Charles, Zachary and Cormode, Graham and Cummings, Rachel and others},
  journal={Foundations and Trends{\textregistered} in Machine Learning},
  volume={14},
  number={1--2},
  pages={1--210},
  year={2021},
  publisher={Now Publishers, Inc.}
}

@InProceedings{BhuyanMM,
author="Bhuyan, Neelkamal
and Moharir, Sharayu
and Joshi, Gauri",
editor="Hyyti{\"a}, Esa
and Kavitha, Veeraruna",
title="Multi-Model Federated Learning with Provable Guarantees",
booktitle="Performance Evaluation Methodologies and Tools",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="207--222",
abstract="Federated Learning (FL) is a variant of distributed learning where edge devices collaborate to learn a model without sharing their data with the central server or each other. We refer to the process of training multiple independent models simultaneously in a federated setting using a common pool of clients as multi-model FL. In this work, we propose two variants of the popular FedAvg algorithm for multi-model FL, with provable convergence guarantees. We further show that for the same amount of computation, multi-model FL can have better performance than training each model separately. We supplement our theoretical results with experiments in strongly convex, convex, and non-convex settings.",
isbn="978-3-031-31234-2"
}

@misc{fednova,
      title={Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization}, 
      author={Jianyu Wang and Qinghua Liu and Hao Liang and Gauri Joshi and H. Vincent Poor},
      year={2020},
      eprint={2007.07481},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{AsyncFL,
  author       = {Cong Xie and
                  Sanmi Koyejo and
                  Indranil Gupta},
  title        = {Asynchronous Federated Optimization},
  journal      = {CoRR},
  volume       = {abs/1903.03934},
  year         = {2019},
  url          = {http://arxiv.org/abs/1903.03934},
  eprinttype    = {arXiv},
  eprint       = {1903.03934},
  timestamp    = {Sun, 31 Mar 2019 19:01:24 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1903-03934.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}


@InProceedings{fedbuff,
  title = 	 { Federated Learning with Buffered Asynchronous Aggregation },
  author =       {Nguyen, John and Malik, Kshitiz and Zhan, Hongyuan and Yousefpour, Ashkan and Rabbat, Mike and Malek, Mani and Huba, Dzmitry},
  booktitle = 	 {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {3581--3607},
  year = 	 {2022},
  editor = 	 {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
  volume = 	 {151},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {28--30 Mar},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v151/nguyen22b/nguyen22b.pdf},
  url = 	 {https://proceedings.mlr.press/v151/nguyen22b.html},
  abstract = 	 { Scalability and privacy are two critical concerns for cross-device federated learning (FL) systems. In this work, we identify that synchronous FL – cannot scale efficiently beyond a few hundred clients training in parallel. It leads to diminishing returns in model performance and training speed, analogous to large-batch training. On the other hand, asynchronous aggregation of client updates in FL (i.e., asynchronous FL) alleviates the scalability issue. However, aggregating individual client updates is incompatible with Secure Aggregation, which could result in an undesirable level of privacy for the system. To address these concerns, we propose a novel buffered asynchronous aggregation method, FedBuff, that is agnostic to the choice of optimizer, and combines the best properties of synchronous and asynchronous FL. We empirically demonstrate that FedBuff is $3.3\times$ more efficient than synchronous FL and up to $2.5\times$ more efficient than asynchronous FL, while being compatible with privacy-preserving technologies such as Secure Aggregation and differential privacy. We provide theoretical convergence guarantees in a smooth non-convex setting. Finally, we show that under differentially private training, FedBuff can outperform FedAvgM at low privacy settings and achieve the same utility for higher privacy settings. }
}

@article{tan2022personalized_FL,
  title={Towards personalized federated learning},
  author={Tan, Alysa Ziying and Yu, Han and Cui, Lizhen and Yang, Qiang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022},
  publisher={IEEE}
}

@inproceedings{li2021ditto_FL,
  title={Ditto: Fair and robust federated learning through personalization},
  author={Li, Tian and Hu, Shengyuan and Beirami, Ahmad and Smith, Virginia},
  booktitle={International Conference on Machine Learning},
  pages={6357--6368},
  year={2021},
  organization={PMLR}
}

@article{mansour2020three_FL,
  title={Three approaches for personalization with applications to federated learning},
  author={Mansour, Yishay and Mohri, Mehryar and Ro, Jae and Suresh, Ananda Theertha},
  journal={arXiv preprint arXiv:2002.10619},
  year={2020}
}

@article{towardsPersonalizedFL,
  author       = {Alireza Fallah and
                  Aryan Mokhtari and
                  Asuman E. Ozdaglar},
  title        = {Personalized Federated Learning: {A} Meta-Learning Approach},
  journal      = {CoRR},
  volume       = {abs/2002.07948},
  year         = {2020},
  url          = {https://arxiv.org/abs/2002.07948},
  eprinttype    = {arXiv},
  eprint       = {2002.07948},
  timestamp    = {Thu, 16 Apr 2020 08:14:42 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2002-07948.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{
Li2020On,
title={On the Convergence of FedAvg on Non-IID Data},
author={Xiang Li and Kaixuan Huang and Wenhao Yang and Shusen Wang and Zhihua Zhang},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=HJxNAnVtDS}
}

@inproceedings{NEURIPS2020_45713f6f,
 author = {Woodworth, Blake E and Patel, Kumar Kshitij and Srebro, Nati},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {6281--6292},
 publisher = {Curran Associates, Inc.},
 title = {Minibatch vs Local SGD for Heterogeneous Distributed Learning},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/45713f6ff2041d3fdfae927b82488db8-Paper.pdf},
 volume = {33},
 year = {2020}
}

@article{Yang2021AchievingLS,
  title={Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning},
  author={Haibo Yang and Minghong Fang and Jia Liu},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.11203},
  url={https://api.semanticscholar.org/CorpusID:231718715}
}

@inproceedings{fedvarp,
  title={Fedvarp: Tackling the variance due to partial client participation in federated learning},
  author={Jhunjhunwala, Divyansh and Sharma, Pranay and Nagarkatti, Aushim and Joshi, Gauri},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={906--916},
  year={2022},
  organization={PMLR}
}

@inproceedings{chen2020asynchronous,
  title={Asynchronous online federated learning for edge devices with non-iid data},
  author={Chen, Yujing and Ning, Yue and Slawski, Martin and Rangwala, Huzefa},
  booktitle={2020 IEEE International Conference on Big Data (Big Data)},
  pages={15--24},
  year={2020},
  organization={IEEE}
}
@inproceedings{
sharper,
title={Sharper Convergence Guarantees for Asynchronous {SGD} for Distributed and Federated Learning},
author={Anastasia Koloskova and Sebastian U Stich and Martin Jaggi},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=4_oCZgBIVI}
}

@misc{persafl,
      title={PersA-FL: Personalized Asynchronous Federated Learning}, 
      author={Mohammad Taha Toghani and Soomin Lee and César A. Uribe},
      year={2022},
      eprint={2210.01176},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{marieMM,
  title={Fair Training of Multiple Federated Learning Models on Resource Constrained Network Devices},
  author={Siew, Marie and Arunasalam, Shoba and Ruan, Yichen and Zhu, Ziwei and Su, Lili and Ioannidis, Stratis and Yeh, Edmund and Joe-Wong, Carlee},
  booktitle={Proceedings of the 22nd International Conference on Information Processing in Sensor Networks},
  pages={330--331},
  year={2023}
}

@article{reisizadeh2019coded,
  title={Coded computation over heterogeneous clusters},
  author={Reisizadeh, Amirhossein and Prakash, Saurav and Pedarsani, Ramtin and Avestimehr, Amir Salman},
  journal={IEEE Transactions on Information Theory},
  volume={65},
  number={7},
  pages={4227--4242},
  year={2019},
  publisher={IEEE}
}

@misc{asyncMM,
      title={Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization}, 
      author={Zhan-Lun Chang and Seyyedali Hosseinalipour and Mung Chiang and Christopher G. Brinton},
      year={2023},
      eprint={2305.13503},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{leaf,
      title={LEAF: A Benchmark for Federated Settings}, 
      author={Sebastian Caldas and Sai Meher Karthik Duddu and Peter Wu and Tian Li and Jakub Konečný and H. Brendan McMahan and Virginia Smith and Ameet Talwalkar},
      year={2019},
      eprint={1812.01097},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@ARTICLE{mnist,
  author={Deng, Li},
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@article{fashionmnist,
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}

@article{cifar10,
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@inproceedings{
feddyn,
title={Federated Learning Based on Dynamic Regularization},
author={Durmus Alp Emre Acar and Yue Zhao and Ramon Matas and Matthew Mattina and Paul Whatmough and Venkatesh Saligrama},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=B7v4QMR6Z9w}
}

@incollection{pytorch, 
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library}, 
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32}, 
pages = {8024--8035}, 
year = {2019}, 
publisher = {Curran Associates, Inc.}, 
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}

@ARTICLE{lenet,
  author={Lecun, Y. and Bottou, L. and Bengio, Y. and Haffner, P.},
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@misc{favano,
      title={FAVANO: Federated AVeraging with Asynchronous NOdes}, 
      author={Louis Leconte and Van Minh Nguyen and Eric Moulines},
      year={2023},
      eprint={2305.16099},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@misc{quafl,
      title={Communication-Efficient Federated Learning With Data and Client Heterogeneity}, 
      author={Hossein Zakerinia and Shayan Talaei and Giorgi Nadiradze and Dan Alistarh},
      year={2023},
      eprint={2206.10032},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{firstDirichlet,
  title={Bayesian nonparametric federated learning of neural networks},
  author={Yurochkin, Mikhail and Agarwal, Mayank and Ghosh, Soumya and Greenewald, Kristjan and Hoang, Nghia and Khazaeni, Yasaman},
  booktitle={International conference on machine learning},
  pages={7252--7261},
  year={2019},
  organization={PMLR}
}

@inproceedings{MM_ucb,
  title={Multi-model federated learning},
  author={Bhuyan, Neelkamal and Moharir, Sharayu},
  booktitle={2022 14th International Conference on COMmunication Systems \& NETworkS (COMSNETS)},
  pages={779--783},
  year={2022},
  organization={IEEE}
}

@inproceedings{MM_bobs,
  title={Efficient device scheduling with multi-job federated learning},
  author={Zhou, Chendi and Liu, Ji and Jia, Juncheng and Zhou, Jingbo and Zhou, Yang and Dai, Huaiyu and Dou, Dejing},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={9},
  pages={9971--9979},
  year={2022}
}

@article{perturbedIterate,
author = {Mania, Horia and Pan, Xinghao and Papailiopoulos, Dimitris and Recht, Benjamin and Ramchandran, Kannan and Jordan, Michael I.},
title = {Perturbed Iterate Analysis for Asynchronous Stochastic Optimization},
journal = {SIAM Journal on Optimization},
volume = {27},
number = {4},
pages = {2202-2229},
year = {2017},
doi = {10.1137/16M1057000},

URL = {https://doi.org/10.1137/16M1057000
},
eprint = {https://doi.org/10.1137/16M1057000
}
}

@ARTICLE{shiftedexp1,
  author={Shi, Wenqi and Zhou, Sheng and Niu, Zhisheng and Jiang, Miao and Geng, Lu},
  journal={IEEE Transactions on Wireless Communications}, 
  title={Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning}, 
  year={2021},
  volume={20},
  number={1},
  pages={453-467},
  doi={10.1109/TWC.2020.3025446}}

@ARTICLE{shiftedexp2,
  author={Lee, Kangwook and Lam, Maximilian and Pedarsani, Ramtin and Papailiopoulos, Dimitris and Ramchandran, Kannan},
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  title={Speeding Up Distributed Machine Learning Using Codes}, 
  year={2018},
  volume={64},
  number={3},
  pages={1514-1529},
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@INPROCEEDINGS{shiftedexp3,
  author={Li, Songze and Maddah-Ali, Mohammad Ali and Avestimehr, A. Salman},
  booktitle={2016 IEEE Globecom Workshops (GC Wkshps)}, 
  title={A Unified Coding Framework for Distributed Computing with Straggling Servers}, 
  year={2016},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/GLOCOMW.2016.7848828}}

@ARTICLE{slowandstale,
  author={Dutta, Sanghamitra and Wang, Jianyu and Joshi, Gauri},
  journal={IEEE Journal on Selected Areas in Information Theory}, 
  title={Slow and Stale Gradients Can Win the Race}, 
  year={2021},
  volume={2},
  number={3},
  pages={1012-1024},
  doi={10.1109/JSAIT.2021.3103770}}

@inproceedings{asynchfl,
author = {Yu, Xiaofan and Cherkasova, Lucy and Vardhan, Harsh and Zhao, Quanling and Ekaireb, Emily and Zhang, Xiyuan and Mazumdar, Arya and Rosing, Tajana},
title = {Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks},
year = {2023},
isbn = {9798400700378},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3576842.3582377},
doi = {10.1145/3576842.3582377},
booktitle = {Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation},
pages = {236–248},
numpages = {13},
keywords = {Hierarchical Sensor and IoT Networks, Asynchronous FL., Federated Learning},
location = {San Antonio, TX, USA},
series = {IoTDI '23}
}

@inproceedings{fedsysdesign,
title	= {Towards Federated Learning at Scale: System Design},
author	= {K. A. Bonawitz and Hubert Eichner and Wolfgang Grieskamp and Dzmitry Huba and Alex Ingerman and Vladimir Ivanov and Chloé M Kiddon and Jakub Konečný and Stefano Mazzocchi and Brendan McMahan and Timon Van Overveldt and David Petrou and Daniel Ramage and Jason Roselander},
year	= {2019},
URL	= {https://arxiv.org/abs/1902.01046},
note	= {To appear},
booktitle	= {SysML 2019}
}

@inproceedings{clientSelectionInHet,
  title={Client selection for federated learning with heterogeneous resources in mobile edge},
  author={Nishio, Takayuki and Yonetani, Ryo},
  booktitle={ICC 2019-2019 IEEE international conference on communications (ICC)},
  pages={1--7},
  year={2019},
  organization={IEEE}
}
@inproceedings{optimalClientSampling,
  title={Tackling system and statistical heterogeneity for federated learning with adaptive client sampling},
  author={Luo, Bing and Xiao, Wenli and Wang, Shiqiang and Huang, Jianwei and Tassiulas, Leandros},
  booktitle={IEEE INFOCOM 2022-IEEE conference on computer communications},
  pages={1739--1748},
  year={2022},
  organization={IEEE}
}


@misc{asyncfeded,
      title={AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation}, 
      author={Qiyuan Wang and Qianqian Yang and Shibo He and Zhiguo Shi and Jiming Chen},
      year={2022},
      eprint={2205.13797},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{fed_car_map,
author = {Zhang, Zijian and Wang, Shuai and Hong, Yuncong and Zhou, Liangkai and Hao, Qi},
title = {Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles},
year = {2021},
publisher = {IEEE Press},
url = {https://doi.org/10.1109/ICRA48506.2021.9561612},
doi = {10.1109/ICRA48506.2021.9561612},
booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
pages = {953–959},
numpages = {7},
location = {Xi'an, China}
}

@ARTICLE{fed_car_6g,
  author={Khan, Latif U. and Tun, Yan Kyaw and Alsenwi, Madyan and Imran, Muhammad and Han, Zhu and Hong, Choong Seon},
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  title={A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars}, 
  year={2022},
  volume={},
  number={},
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@ARTICLE{iotsurvey,
  author={Khan, Latif U. and Saad, Walid and Han, Zhu and Hossain, Ekram and Hong, Choong Seon},
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@inproceedings{improvingComm,
title	= {Federated Learning: Strategies for Improving Communication Efficiency},
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URL	= {https://arxiv.org/abs/1610.05492},
booktitle	= {NIPS Workshop on Private Multi-Party Machine Learning}
}

@article{wang2022unified,
  title={A unified analysis of federated learning with arbitrary client participation},
  author={Wang, Shiqiang and Ji, Mingyue},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={19124--19137},
  year={2022}
}

@inproceedings{karimireddy2020scaffold,
  title={Scaffold: Stochastic controlled averaging for federated learning},
  author={Karimireddy, Sai Praneeth and Kale, Satyen and Mohri, Mehryar and Reddi, Sashank and Stich, Sebastian and Suresh, Ananda Theertha},
  booktitle={International conference on machine learning},
  pages={5132--5143},
  year={2020},
  organization={PMLR}
}

@InProceedings{powerofchoice,
  title = 	 { Towards Understanding Biased Client Selection in Federated Learning },
  author =       {Cho, Yae Jee and Wang, Jianyu and Joshi, Gauri},
  booktitle = 	 {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {10351--10375},
  year = 	 {2022},
  editor = 	 {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
  volume = 	 {151},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {28--30 Mar},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v151/jee-cho22a/jee-cho22a.pdf},
  url = 	 {https://proceedings.mlr.press/v151/jee-cho22a.html}
}


@ARTICLE{feddiffpriv,
  author={Wei, Kang and Li, Jun and Ding, Ming and Ma, Chuan and Yang, Howard H. and Farokhi, Farhad and Jin, Shi and Quek, Tony Q. S. and Vincent Poor, H.},
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  year={2020},
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  number={},
  pages={3454-3469},
  doi={10.1109/TIFS.2020.2988575}}

@inproceedings{secureAggr,
title	= {Practical Secure Aggregation for Federated Learning on User-Held Data},
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@article{richtarikSubsampling,
  author       = {Wenlin Chen and
                  Samuel Horv{\'{a}}th and
                  Peter Richt{\'{a}}rik},
  title        = {Optimal Client Sampling for Federated Learning},
  journal      = {CoRR},
  volume       = {abs/2010.13723},
  year         = {2020},
  url          = {https://arxiv.org/abs/2010.13723},
  eprinttype    = {arXiv},
  eprint       = {2010.13723},
  timestamp    = {Sun, 02 Oct 2022 15:32:05 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2010-13723.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@INPROCEEDINGS{flsim,
  author={Wang, Hao and Kaplan, Zakhary and Niu, Di and Li, Baochun},
  booktitle={IEEE INFOCOM 2020 - IEEE Conference on Computer Communications}, 
  title={Optimizing Federated Learning on Non-IID Data with Reinforcement Learning}, 
  year={2020},
  volume={},
  number={},
  pages={1698-1707},
  doi={10.1109/INFOCOM41043.2020.9155494}}

@inproceedings{mmcars,
  title={FLOW: A Scalable Multi-Model Federated Learning Framework on the Wheels},
  author={Yao, Yongtao and Ammar, Nejib and Shi, Weisong},
  booktitle={2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)},
  pages={11--22},
  year={2023},
  organization={IEEE}
}

@article{after_review1,
  title={Robust federated learning with noisy communication},
  author={Ang, Fan and Chen, Li and Zhao, Nan and Chen, Yunfei and Wang, Weidong and Yu, F Richard},
  journal={IEEE Transactions on Communications},
  volume={68},
  number={6},
  pages={3452--3464},
  year={2020},
  publisher={IEEE}
}

@article{after_review2,
  title={On the convergence of decentralized federated learning under imperfect information sharing},
  author={Chellapandi, Vishnu Pandi and Upadhyay, Antesh and Hashemi, Abolfazl and {\.Z}ak, Stanislaw H},
  journal={IEEE Control Systems Letters},
  year={2023},
  publisher={IEEE}
}

@inproceedings{after_review3,
author = {Yu, Jieling and Zhou, Ruiting and Chen, Chen and Li, Bo and Dong, Fang},
title = {ASFL: Adaptive Semi-asynchronous Federated Learning for Balancing Model Accuracy and Total Latency in Mobile Edge Networks},
year = {2023},
isbn = {9798400708435},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3605573.3605582},
doi = {10.1145/3605573.3605582},
booktitle = {Proceedings of the 52nd International Conference on Parallel Processing},
pages = {443–451},
numpages = {9},
keywords = {Semi-asynchronous, Lag tolerance, Federated learning, Deep reinforcement learning},
location = {<conf-loc>, <city>Salt Lake City</city>, <state>UT</state>, <country>USA</country>, </conf-loc>},
series = {ICPP '23}
}

@article{airquality,
  title={Insights into multi-model federated learning: An advanced approach for air quality index forecasting},
  author={Le, Duy-Dong and Tran, Anh-Khoa and Dao, Minh-Son and Nguyen-Ly, Kieu-Chinh and Le, Hoang-Son and Nguyen-Thi, Xuan-Dao and Pham, Thanh-Qui and Nguyen, Van-Luong and Nguyen-Thi, Bach-Yen},
  journal={Algorithms},
  volume={15},
  number={11},
  pages={434},
  year={2022},
  publisher={MDPI}
}

@ARTICLE{asfl_paper,
  author={Liu, Jianchun and Xu, Hongli and Wang, Lun and Xu, Yang and Qian, Chen and Huang, Jinyang and Huang, He},
  journal={IEEE Transactions on Mobile Computing}, 
  title={Adaptive Asynchronous Federated Learning in Resource-Constrained Edge Computing}, 
  year={2023},
  volume={22},
  number={2},
  pages={674-690},
  keywords={Servers;Training;Computational modeling;Data models;Adaptation models;Collaborative work;Task analysis;Edge computing;asynchronous federated learning;adaptive;resource constraint},
  doi={10.1109/TMC.2021.3096846}}
