@inproceedings{breunig2000lof,
  title={LOF: identifying density-based local outliers},
  author={Breunig, Markus M and Kriegel, Hans-Peter and Ng, Raymond T and Sander, J{\"o}rg},
  booktitle={Proceedings of the 2000 ACM SIGMOD international conference on Management of data},
  pages={93--104},
  year={2000}
}

@article{zhang2022introduction,
  title={An introduction to the federated learning standard},
  author={Zhang, Ticao and Mao, Shiwen},
  journal={GetMobile: Mobile Computing and Communications},
  volume={25},
  number={3},
  pages={18--22},
  year={2022},
  publisher={ACM New York, NY, USA}
}

@article{li2021survey,
  title={A survey on federated learning systems: Vision, hype and reality for data privacy and protection},
  author={Li, Qinbin and Wen, Zeyi and Wu, Zhaomin and Hu, Sixu and Wang, Naibo and Li, Yuan and Liu, Xu and He, Bingsheng},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2021},
  publisher={IEEE}
}

@article{li2020review,
  title={A review of applications in federated learning},
  author={Li, Li and Fan, Yuxi and Tse, Mike and Lin, Kuo-Yi},
  journal={Computers \& Industrial Engineering},
  volume={149},
  pages={106854},
  year={2020},
  publisher={Elsevier}
}

@article{wei2020federated,
  title={Federated learning with differential privacy: Algorithms and performance analysis},
  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 QS and Poor, H Vincent},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={15},
  pages={3454--3469},
  year={2020},
  publisher={IEEE}
}

@article{khan2021federated,
  title={Federated learning for internet of things: Recent advances, taxonomy, and open challenges},
  author={Khan, Latif U and Saad, Walid and Han, Zhu and Hossain, Ekram and Hong, Choong Seon},
  journal={IEEE Communications Surveys \& Tutorials},
  volume={23},
  number={3},
  pages={1759--1799},
  year={2021},
  publisher={IEEE}
}

@article{ye2020edgefed,
  title={EdgeFed: Optimized federated learning based on edge computing},
  author={Ye, Yunfan and Li, Shen and Liu, Fang and Tang, Yonghao and Hu, Wanting},
  journal={IEEE Access},
  volume={8},
  pages={209191--209198},
  year={2020},
  publisher={IEEE}
}

@article{hsu2019measuring,
  title={Measuring the effects of non-identical data distribution for federated visual classification},
  author={Hsu, Tzu-Ming Harry and Qi, Hang and Brown, Matthew},
  journal={arXiv preprint arXiv:1909.06335},
  year={2019}
}

@inproceedings{bagdasaryan2020backdoor,
  title={How to backdoor federated learning},
  author={Bagdasaryan, Eugene and Veit, Andreas and Hua, Yiqing and Estrin, Deborah and Shmatikov, Vitaly},
  booktitle={International conference on artificial intelligence and statistics},
  pages={2938--2948},
  year={2020},
  organization={PMLR}
}

@inproceedings{shejwalkar2022back,
  title={Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning},
  author={Shejwalkar, Virat and Houmansadr, Amir and Kairouz, Peter and Ramage, Daniel},
  booktitle={2022 IEEE Symposium on Security and Privacy (SP)},
  pages={1354--1371},
  year={2022},
  organization={IEEE}
}

@inproceedings{li2019rsa,
  title={RSA: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets},
  author={Li, Liping and Xu, Wei and Chen, Tianyi and Giannakis, Georgios B and Ling, Qing},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  number={01},
  pages={1544--1551},
  year={2019}
}

@article{wu2020federated,
  title={Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks},
  author={Wu, Zhaoxian and Ling, Qing and Chen, Tianyi and Giannakis, Georgios B},
  journal={IEEE Transactions on Signal Processing},
  volume={68},
  pages={4583--4596},
  year={2020},
  publisher={IEEE}
}

@inproceedings{qureshi2022poisoning,
  title={Poisoning attacks against federated learning in load forecasting of smart energy},
  author={Qureshi, Naik Bakht Sania and Kim, Dong-Hoon and Lee, Jiwoo and Lee, Eun-Kyu},
  booktitle={NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium},
  pages={1--7},
  year={2022},
  organization={IEEE}
}

@inproceedings{qureshi2021performance,
  title={On the performance impact of poisoning attacks on load forecasting in federated learning},
  author={Qureshi, Naik Bakht Sania and Kim, Dong-Hoon and Lee, Jiwoo and Lee, Eun-Kyu},
  booktitle={Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},
  pages={64--66},
  year={2021}
}

@article{blanchard2017machine,
  title={Machine learning with adversaries: Byzantine tolerant gradient descent},
  author={Blanchard, Peva and El Mhamdi, El Mahdi and Guerraoui, Rachid and Stainer, Julien},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{shejwalkar2021manipulating,
  title={Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning},
  author={Shejwalkar, Virat and Houmansadr, Amir},
  booktitle={NDSS},
  year={2021}
}
@article{sattler2020clustered2,
  title={Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints},
  author={Sattler, Felix and M{\"u}ller, Klaus-Robert and Samek, Wojciech},
  journal={IEEE transactions on neural networks and learning systems},
  volume={32},
  number={8},
  pages={3710--3722},
  year={2020},
  publisher={IEEE}
}

@INPROCEEDINGS{sattler2020clustered,

  author={Sattler, Felix and Müller, Klaus-Robert and Wiegand, Thomas and Samek, Wojciech},

  booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 

  title={On the Byzantine Robustness of Clustered Federated Learning}, 

  year={2020},

  volume={},

  number={},

  pages={8861-8865},

  keywords={Training;Sociology;Signal processing;Collaborative work;Robustness;Statistics;Speech processing;robust learning;distributed learning;Federated Learning;multi-task learning;clustering},

  doi={10.1109/ICASSP40776.2020.9054676}}


@ARTICLE{9650669,

  author={Li, Xingyu and Qu, Zhe and Zhao, Shangqing and Tang, Bo and Lu, Zhuo and Liu, Yao},

  journal={IEEE Transactions on Dependable and Secure Computing}, 

  title={LoMar: A Local Defense Against Poisoning Attack on Federated Learning}, 

  year={2023},

  volume={20},

  number={1},

  pages={437-450},

  doi={10.1109/TDSC.2021.3135422}}

@article{bandi2018detection,
  title={From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge},
  author={Bandi, Peter and Geessink, Oscar and Manson, Quirine and Van Dijk, Marcory and Balkenhol, Maschenka and Hermsen, Meyke and Bejnordi, Babak Ehteshami and Lee, Byungjae and Paeng, Kyunghyun and Zhong, Aoxiao and others},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={2},
  pages={550--560},
  year={2018},
  publisher={IEEE}
}

@article{kather2018100,
  title={100,000 histological images of human colorectal cancer and healthy tissue},
  author={Kather, Jakob Nikolas and Halama, Niels and Marx, Alexander},
  journal={Zenodo10},
  volume={5281},
  year={2018}
}

@article{li2020learning,
  title={Learning to detect malicious clients for robust federated learning},
  author={Li, Suyi and Cheng, Yong and Wang, Wei and Liu, Yang and Chen, Tianjian},
  journal={arXiv preprint arXiv:2002.00211},
  year={2020}
}

@article{FLMed, title={Federated learning and differential privacy for medical image analysis}, 
volume={12}, 
url={https://doi.org/10.1038/s41598-022-05539-7}, DOI={10.1038/s41598-022-05539-7}, 
journal={Scientific Reports}, 
author={Adnan, Mohammed and Kalra, Shivam and Cresswell, Jesse C. and Taylor, Graham W. and Tizhoosh, Hamid R.}, 
year={2022}, 
month={Feb.}, 
issn={2045-2322} }

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}

@INPROCEEDINGS{7780459,

  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},

  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 

  title={Deep Residual Learning for Image Recognition}, 

  year={2016},

  volume={},

  number={},

  pages={770-778},

  doi={10.1109/CVPR.2016.90}}


@inproceedings{liu2015faceattributes,
  title = {Deep Learning Face Attributes in the Wild},
  author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
  month = {December},
  year = {2015} 
}

@article{stieber2022froodo,
  title={FrOoDo: Framework for Out-of-Distribution Detection},
  author={Stieber, Jonathan and Fuchs, Moritz and Mukhopadhyay, Anirban},
  journal={arXiv preprint arXiv:2208.00963},
  year={2022}
}

@article{geiping2020inverting,
  title={Inverting gradients-how easy is it to break privacy in federated learning?},
  author={Geiping, Jonas and Bauermeister, Hartmut and Dr{\"o}ge, Hannah and Moeller, Michael},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={16937--16947},
  year={2020}
}

@article{rieke2020future,
  title={The future of digital health with federated learning},
  author={Rieke, Nicola and Hancox, Jonny and Li, Wenqi and Milletari, Fausto and Roth, Holger R and Albarqouni, Shadi and Bakas, Spyridon and Galtier, Mathieu N and Landman, Bennett A and Maier-Hein, Klaus and others},
  journal={NPJ digital medicine},
  volume={3},
  number={1},
  pages={119},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@article{ng2021federated,
  title={Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets},
  author={Ng, Dianwen and Lan, Xiang and Yao, Melissa Min-Szu and Chan, Wing P and Feng, Mengling},
  journal={Quantitative Imaging in Medicine and Surgery},
  volume={11},
  number={2},
  pages={852},
  year={2021},
  publisher={AME Publications}
}

@article{nguyen2022federated,
  title={Federated learning for smart healthcare: A survey},
  author={Nguyen, Dinh C and Pham, Quoc-Viet and Pathirana, Pubudu N and Ding, Ming and Seneviratne, Aruna and Lin, Zihuai and Dobre, Octavia and Hwang, Won-Joo},
  journal={ACM Computing Surveys (CSUR)},
  volume={55},
  number={3},
  pages={1--37},
  year={2022},
  publisher={ACM New York, NY}
}
% WSI acquistion processe
@article{kanwal2022devil,
  title={The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review},
  author={Kanwal, Neel and P{\'e}rez-Bueno, Fernando and Schmidt, Arne and Engan, Kjersti and Molina, Rafael},
  journal={IEEE Access},
  year={2022},
  publisher={IEEE}
}
% Stain Normalization
@inproceedings{
faryna2021tailoring,
title={Tailoring automated data augmentation to H\&E-stained histopathology},
author={Khrystyna Faryna and Jeroen van der Laak and Geert Litjens},
booktitle={Medical Imaging with Deep Learning},
year={2021},
}
% Stain normalization
@inproceedings{wagner2022federated,
  title={Federated Stain Normalization for Computational Pathology},
  author={Wagner, Nicolas and Fuchs, Moritz and Tolkach, Yuri and Mukhopadhyay, Anirban},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={14--23},
  year={2022},
  organization={Springer}
}
% federation under synthetic shifts
@article{babendererde2023jointly,
  title={Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning},
  author={Babendererde, Niklas and Fuchs, Moritz and Gonzalez, Camila and Tolkach, Yuri and Mukhopadhyay, Anirban},
  journal={arXiv preprint arXiv:2309.00688},
  year={2023}
}
%Yuri artifacts
@article{schomig2021quality,
  title={Quality control stress test for deep learning-based diagnostic model in digital pathology},
  author={Sch{\"o}mig-Markiefka, Birgid and Pryalukhin, Alexey and Hulla, Wolfgang and Bychkov, Andrey and Fukuoka, Junya and Madabhushi, Anant and Achter, Viktor and Nieroda, Lech and B{\"u}ttner, Reinhard and Quaas, Alexander and others},
  journal={Modern Pathology},
  year={2021},
  publisher={Nature Publishing Group}
}
@article{griem2023artificial,
  title={Artificial intelligence-based tool for tumor detection and quantitative tissue analysis in colorectal specimens},
  author={Griem, Johanna and Eich, Marie-Lisa and Schallenberg, Simon and Pryalukhin, Alexey and Bychkov, Andrey and Fukuoka, Junya and Zayats, Vitaliy and Hulla, Wolfgang and Munkhdelger, Jijgee and Seper, Alexander and others},
  journal={Modern Pathology},

  year={2023},
  publisher={Elsevier}
}
%DL in DP Breast
@article{couture2018image,
  title={Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype},
  author={Couture, Heather D and Williams, Lindsay A and Geradts, Joseph and Nyante, Sarah J and Butler, Ebonee N and Marron, JS and Perou, Charles M and Troester, Melissa A and Niethammer, Marc},
  journal={NPJ breast cancer},
  year={2018},
  publisher={Nature Publishing Group}
}
%Unreliable clients
@article{ma2021federated,
  title={Federated learning with unreliable clients: Performance analysis and mechanism design},
  author={Ma, Chuan and Li, Jun and Ding, Ming and Wei, Kang and Chen, Wen and Poor, H Vincent},
  journal={IEEE Internet of Things Journal},
  volume={8},
  number={24},
  pages={17308--17319},
  year={2021},
  publisher={IEEE}
}

% Attacks
@ARTICLE{9308910,

  author={Jere, Malhar S. and Farnan, Tyler and Koushanfar, Farinaz},

  journal={IEEE Security & Privacy}, 

  title={A Taxonomy of Attacks on Federated Learning}, 

  year={2021},

  volume={19},

  number={2},

  pages={20-28},

  doi={10.1109/MSEC.2020.3039941}}

# Artifacts 
@INPROCEEDINGS{8713350,

  author={Foucart, Adrien and Debeir, Olivier and Decaestecker, Christine},

  booktitle={2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech)}, 

  title={Artifact Identification in Digital Pathology from Weak and Noisy Supervision with Deep Residual Networks}, 

  year={2018},

  volume={},

  number={},

  pages={1-6},

  doi={10.1109/CloudTech.2018.8713350}}

%Applications
@article{yang2019federated,
  title={Federated machine learning: Concept and applications},
  author={Yang, Qiang and Liu, Yang and Chen, Tianjian and Tong, Yongxin},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume={10},
  number={2},
  pages={1--19},
  year={2019},
  publisher={ACM New York, NY, USA}
}

%Data Regulations
@article{TRUONG2021102402,
title = {Privacy preservation in federated learning: An insightful survey from the GDPR perspective},
journal = {Computers & Security},
volume = {110},
pages = {102402},
year = {2021},
issn = {0167-4048},
doi = {https://doi.org/10.1016/j.cose.2021.102402},
url = {https://www.sciencedirect.com/science/article/pii/S0167404821002261},
author = {Nguyen Truong and Kai Sun and Siyao Wang and Florian Guitton and YiKe Guo},
keywords = {Federated learning, Data protection regulation, GDPR, Personal data, Privacy, Privacy preservation},
abstract = {In recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security have become a critical obligation. ML-based service providers not only confront with difficulties in collecting and managing data across heterogeneous sources but also challenges of complying with rigorous data protection regulations such as EU/UK General Data Protection Regulation (GDPR). Furthermore, conventional centralised ML approaches have always come with long-standing privacy risks to personal data leakage, misuse, and abuse. Federated learning (FL) has emerged as a prospective solution that facilitates distributed collaborative learning without disclosing original training data. Unfortunately, retaining data and computation on-device as in FL are not sufficient for privacy-guarantee because model parameters exchanged among participants conceal sensitive information that can be exploited in privacy attacks. Consequently, FL-based systems are not naturally compliant with the GDPR. This article is dedicated to surveying of state-of-the-art privacy-preservation techniques in FL in relations with GDPR requirements. Furthermore, insights into the existing challenges are examined along with the prospective approaches following the GDPR regulatory guidelines that FL-based systems shall implement to fully comply with the GDPR.}
}

% Expensive annotations
@article{tajbakhsh2021guest,
  title={Guest editorial annotation-efficient deep learning: the holy grail of medical imaging},
  author={Tajbakhsh, Nima and Roth, Holger and Terzopoulos, Demetri and Liang, Jianming},
  journal={IEEE transactions on medical imaging},
  volume={40},
  number={10},
  pages={2526--2533},
  year={2021},
  publisher={IEEE}
}

%Access to heterogenous data
@article{willemink2020preparing,
  title={Preparing medical imaging data for machine learning},
  author={Willemink, Martin J and Koszek, Wojciech A and Hardell, Cailin and Wu, Jie and Fleischmann, Dominik and Harvey, Hugh and Folio, Les R and Summers, Ronald M and Rubin, Daniel L and Lungren, Matthew P},
  journal={Radiology},
  volume={295},
  number={1},
  pages={4--15},
  year={2020},
  publisher={Radiological Society of North America}
}

%Multi institutional FL
@inproceedings{guo2021multi,
  title={Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning},
  author={Guo, Pengfei and Wang, Puyang and Zhou, Jinyuan and Jiang, Shanshan and Patel, Vishal M},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2423--2432},
  year={2021}
}

%% Became popular during the last years
@article{mammen2021federated,
  title={Federated learning: Opportunities and challenges},
  author={Mammen, Priyanka Mary},
  journal={arXiv preprint arXiv:2101.05428},
  year={2021}
}

%% Label Flip Attack
@inproceedings{bhagoji2019analyzing,
  title={Analyzing federated learning through an adversarial lens},
  author={Bhagoji, Arjun Nitin and Chakraborty, Supriyo and Mittal, Prateek and Calo, Seraphin},
  booktitle={International Conference on Machine Learning},
  pages={634--643},
  year={2019},
  organization={PMLR}
}

%% Random Attacks
@inproceedings{fang2020local,
  title={Local model poisoning attacks to $\{$Byzantine-Robust$\}$ federated learning},
  author={Fang, Minghong and Cao, Xiaoyu and Jia, Jinyuan and Gong, Neil},
  booktitle={29th USENIX security symposium (USENIX Security 20)},
  pages={1605--1622},
  year={2020}
}

%% Imagenet
@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE conference on computer vision and pattern recognition},
  pages={248--255},
  year={2009},
  organization={Ieee}
}


@article{paszke2019pytorch,
  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 others},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

%% FL Trust
@article{cao2020fltrust,
  title={Fltrust: Byzantine-robust federated learning via trust bootstrapping},
  author={Cao, Xiaoyu and Fang, Minghong and Liu, Jia and Gong, Neil Zhenqiang},
  journal={arXiv preprint arXiv:2012.13995},
  year={2020}
}

%% ShieldFL
@article{ma2022shieldfl,
  title={ShieldFL: Mitigating model poisoning attacks in privacy-preserving federated learning},
  author={Ma, Zhuoran and Ma, Jianfeng and Miao, Yinbin and Li, Yingjiu and Deng, Robert H},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={17},
  pages={1639--1654},
  year={2022},
  publisher={IEEE}
}

%% K - Means
@inproceedings{li2021detection,
  title={Detection and mitigation of label-flipping attacks in federated learning systems with KPCA and K-means},
  author={Li, Dongcheng and Wong, W Eric and Wang, Wei and Yao, Yao and Chau, Matthew},
  booktitle={2021 8th International Conference on Dependable Systems and Their Applications (DSA)},
  pages={551--559},
  year={2021},
  organization={IEEE}
}

%% SFAP
@article{ma2021pocket,
  title={Pocket diagnosis: Secure federated learning against poisoning attack in the cloud},
  author={Ma, Zhuoran and Ma, Jianfeng and Miao, Yinbin and Liu, Ximeng and Choo, Kim-Kwang Raymond and Deng, Robert H},
  journal={IEEE Transactions on Services Computing},
  volume={15},
  number={6},
  pages={3429--3442},
  year={2021},
  publisher={IEEE}
}

@article{sisodia2012clustering,
  title={Clustering techniques: a brief survey of different clustering algorithms},
  author={Sisodia, Deepti and Singh, Lokesh and Sisodia, Sheetal and Saxena, Khushboo},
  journal={International Journal of Latest Trends in Engineering and Technology (IJLTET)},
  volume={1},
  number={3},
  pages={82--87},
  year={2012}
}
