references.bib

@article{medmnist,
     title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
     author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
     journal={arXiv preprint arXiv:2010.14925},
     year={2020}
}
@misc{gdpr,
    title={General Data Protection Regulation},
    url={https://gdpr-info.eu/}
}
@misc{hipaa,
    title={Health Insurance Portability and Accountability Act},
    url={https://www.cdc.gov/phlp/publications/topic/hipaa.html}
}
@misc{federated_comic,
    title="Federated Learning - Building better products with on-device data and privacy by default",
    url={https://federated.withgoogle.com/}, 
    publisher={Google AI}
}
@incollection{neurons,
    author={JJ Hopfield},
    title={Neural networks and physical systems with emergent collective computational abilities},
}
@misc{relu,
    author={V. Nair, G.E. Hinton},
    title={Rectified linear units improve restricted boltzmann machines},
    year={2010},
    url={https://dl.acm.org/doi/10.5555/3104322.3104425},
}
@misc{wiki:activation,
   author = "{Wikipedia contributors}",
   title = "Activation Function Table --- {W}ikipedia{,} The Free Encyclopedia",
   year = "2021",
   url = "https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions",
   note = "[Online; accessed 16-Jan-2021]"
}
@misc{activation_combined,
    author={F. Manessi, A. Rozza},
    title={Learning Combinations of Activation Functions},
    url={https://arxiv.org/pdf/1801.09403.pdf},
}
@book{deeplearningbook,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
}
@article{eli5_convnet,
    author={S. Saha},
    title={A comprehensive Guide to Convolutional Neural Networks},
    url={https://towardsdatascience.com/3bd2b1164a53},
    year={2018}
}
@article{mnist,
    added-at = {2010-06-28T21:16:30.000+0200},
    author = {LeCun, Yann and Cortes, Corinna},
    biburl = {https://www.bibsonomy.org/bibtex/2935bad99fa1f65e03c25b315aa3c1032/mhwombat},
    groups = {public},
    howpublished = {http://yann.lecun.com/exdb/mnist/},
    interhash = {21b9d0558bd66279df9452562df6e6f3},
    intrahash = {935bad99fa1f65e03c25b315aa3c1032},
    keywords = {MSc _checked character_recognition mnist network neural},
    lastchecked = {2016-01-14 14:24:11},
    timestamp = {2016-07-12T19:25:30.000+0200},
    title = {{MNIST} handwritten digit database},
    url = {http://yann.lecun.com/exdb/mnist/},
    username = {mhwombat},
    year = 2010
}
@online{fashion,
    author       = {Han Xiao and Kashif Rasul and Roland Vollgraf},
    title        = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
    date         = {2017-08-28},
    year         = {2017},
    eprintclass  = {cs.LG},
    eprinttype   = {arXiv},
    eprint       = {cs.LG/1708.07747},
}
@misc{cifar,
    author={A. Krizhevsky},
    title={Learning Multiple Layers of Features from Tiny images},
    date={2009-04-08},
    url={http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf}
}
@misc{convnet,
    author={K. O'Shea, R. Nash},
    title={An Introduction to Convolutional Neural Networks},
    date={2015-12-02},
    url={https://arxiv.org/pdf/1511.08458.pdf}
}
@misc{failed_anonymisation,
    author={L. Sweeney},
    title={Matching Known Patients to Health Records in Washington State Data},
    date={2013-07-04},
    url={https://arxiv.org/ftp/arxiv/papers/1307/1307.1370.pdf}
}
@misc{anon_cluster,
    author={J. Byun, A. Kamra, E. Bertino, N. Li},
    title={Efficient k-Anonymization Using Clustering Techniques},
    url={https://www.cs.purdue.edu/homes/ninghui/papers/clustering_dasfaa07.pdf}
}
@inbook{fhe,
    author={R. Rivest, L. Adleman, M. Dertouzos},
    title={On Data Banks and Privacy Homomorphisms},
    year={1978},
    note={pages 169-180}
}
@article{access_private,
    added-at = {2010-09-12T21:19:00},
    author = {A. Chen},
    title = {Google Engineer Allegedly Fired For Accessing Private User Information To Stalk Teens},
    url = {https://www.businessinsider.com/google-engineer-stalked-teens-spied-on-chats-2010-9?r=US&IR=T},
    year = 2010
}
@incollection{cloud_fhe,
    title = "Chapter 5 - A guide to homomorphic encryption",
    editor = "Ryan Ko and Kim-Kwang Raymond Choo",
    booktitle = "The Cloud Security Ecosystem",
    publisher = "Syngress",
    address = "Boston",
    pages = "101 - 127",
    year = "2015",
    isbn = "978-0-12-801595-7",
    doi = "https://doi.org/10.1016/B978-0-12-801595-7.00005-7",
    url = "http://www.sciencedirect.com/science/article/pii/B9780128015957000057",
    author = "Mark A. Will and Ryan K.L. Ko",
    keywords = "Homomorphic Encryption, Cloud Security, Fully Homomorphic Encryption, Partial Homomorphic Encryption, Data Privacy",
    abstract = "Traditional cryptography techniques require our data to be unencrypted to be processed correctly. This means that at some stage on a system we have no control over, our data will be processed in plaintext. Homomorphic encryption or specifically, fully homomorphic encryption is a viable solution to this problem. It allows encrypted data to be processed as if it were in plaintext and will produce the correct value once decrypted. While many know that homomorphic encryption promises to be an ideal solution to trust, security, and privacy issues in cloud computing, few actually knows how it works and why it is not yet a practical solution despite its promises. This chapter serves as a much needed primer on current homomorphic encryption techniques, discusses about several practical challenges, and introduces workarounds proposed by practitioners and researchers to overcome these challenges."
}
@misc{bgw,
    title={Completeness Theorems for Non-Cryptographic Fault-Tolerant Distributed Computation}
    author={M. Ben-Or, S. Goldwasser, A. Wigderson}
    year={1988}
}
@article{global_dp,
    added-at = {2019-07-18},
    author = {C. Mircea},
    title = {Introducing Local and Global Differential Privacy - Lesson 5},
    url = {https://medium.com/secure-and-private-ai-writing-challenge/7ae9edea57c9},
}
@misc{dnn_noise,
    author={A. Neelakantan, L. Vilnis, Q. Le, I. Sutskever, L. Kaiser, K. Kurach, J. Martens},
    title={Adding Gradient Noise Improves Learning for Very Depp Networks},
    url={https://arxiv.org/pdf/1511.06807.pdf}
}
@misc{robust_corrupt_noise,
    author={E. Rusak, L. Schott, R. Zimmermann, J. Bitterwolf, O. Bringmann, M. Bethge, W. Brendel},
    title={A simple way to make neural networks robust against diverse image corruptions},
    url={https://arxiv.org/pdf/2001.06057.pdf}
}
@misc{future_health_fl,
    author={N. Rieke, J. Hancox, W Li, F. Milletari, H. Roth, S. Albarqouni, S. Bakas, M. Galtier, B. Landman, K. Maier-Hein, S. Ourselin, M. Sheller, R. Summers, A. Trask, D. Xu, M. Baust and M. Cardoso},
    title={The Future of Digital Health with Federated Learning},
    url={https://arxiv.org/pdf/2003.08119.pdf}
}
@misc{fedavg,
    author={H. McMahan, E. Moore, D Ramage, S. Hampson, B. Aguera y Arcas},
    title={Communication-Efficient Learning of Deep Networks from Decentralized Data},
    url={https://arxiv.org/pdf/1602.05629.pdf}
}
@misc{fedsgd,
    author={R, Shokri, V. Shmatikov},
    title={Privacy-Preserving Deep Learning},
    url={https://www.cs.cornell.edu/~shmat/shmat_ccs15.pdf}
}
@misc{robagg_health,
    author={M. Grama, M. Musat, L. Muñoz-González, J. Passerat-Palmbach, D. Rueckert, A. Alansary},
    title={Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare},
    url={https://arxiv.org/pdf/2009.08294.pdf}
}
@article{federated_learning,
    added-at = {2019-04-06},
    author = {U. Bhat},
    title = {A Beginners Guide to Federated Learning},
    url = {https://hackernoon.com/a-beginners-guide-to-federated-learning-b29e29ba65cf},
}
@misc{robagg_fl,
    author={K. Pillutla, S. Kakade, Z. Harchaoui},
    title={Robust Aggregation for Federated Learning},
    url={https://arxiv.org/pdf/1912.13445.pdf}
}
@misc{poison_dnn,
    author={X. Chen, C. Liu, B. Li, K. Lu, D. Song},
    title={Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning},
    url={https://arxiv.org/pdf/1712.05526.pdf}
}
@misc{adversarial_lens,
    author={A. Bhagoji, S. Chakraborty, P. Mittal, S. Calo},
    title={Analyzing Federated Learning through an Adversarial Lens},
    url={https://arxiv.org/pdf/1811.12470.pdf}
}
@misc{babu,
    author={E. Babu},
    title={Federated Deep Learning for Healthcare Data}
}
@inproceedings{krum,
    author = {Blanchard, Peva and El Mhamdi, El Mahdi and Guerraoui, Rachid and Stainer, Julien},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
    pages = {119--129},
    publisher = {Curran Associates, Inc.},
    title = {Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent},
    url = {https://proceedings.neurips.cc/paper/2017/file/f4b9ec30ad9f68f89b29639786cb62ef-Paper.pdf},
    volume = {30},
    year = {2017}
}
@misc{comed,
    title={Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates}, 
    author={Dong Yin and Yudong Chen and Kannan Ramchandran and Peter Bartlett},
    year={2018},
    url={https://arxiv.org/pdf/1803.01498.pdf}
}
@misc{afa,
    title={Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging}, 
    author={Luis Muñoz-González and Kenneth T. Co and Emil C. Lupu},
    year={2019},
    url={https://arxiv.org/pdf/1909.05125.pdf}
}
@misc{nhs_digital_data,
    title={Keeping Patient Data Safe},
    url={https://digital.nhs.uk/about-nhs-digital/our-work/keeping-patient-data-safe}
}
@misc{kernel,
    author={Victor Powell},
    title={Image Kernels Explained Visually},
    url={https://setosa.io/ev/image-kernels/}
}
@misc{fedmgda,
    author={Z. Hu, K. Shaloudegi, G. Zhang, Y. Yu},
    title={FedMGDA+: Federated Learning meets Multi-objective Optimization},
    url={https://arxiv.org/pdf/2006.11489.pdf}
}
@misc{cluster_robagg,
    author={L. Yu, L, Wu},
    title={Towards Byzantine-Resilient Federated Learning via Group-Wise Robust Aggregation},
    url={https://link.springer.com/chapter/10.1007/978-3-030-63076-8_6}
}
@unpublished{priv_eng,
    author={N. Dulay, Y. de Montjoye},
    title={Privacy Engineering},
    url={https://www.imperial.ac.uk/computing/current-students/courses/70018/}
}
@misc{spectral,
    author={S. Li, Y. Cheng, W. Wang, Y. Liu, T. Chen},
    title={Learning to Detect Malicious Clients for Robust Federated Learning},
    url={https://arxiv.org/pdf/2002.00211.pdf}
}
@article{sad,
    author = {V. Chandola, A. Banerjee, V. Kumar},
    title = {Anomaly Detection: A Survey},
    year = {2009},
    issue_date = {July 2009},
    url = {https://doi.org/10.1145/1541880.1541882},
}
@inproceedings{variationalAB,
    title={Variational Autoencoder based Anomaly Detection using Reconstruction Probability},
    author={Jinwon An and S. Cho},
    year={2015}
}
@misc{github,
    author={S. Trew},
    title={FL Medical},
    url={https://github.com/SamuelTrew/FLmedical/}
}
@misc{free_riding,
    author={Y. Fraboni, R. Vidal, M.Lorenzi},
    title={Free-rider attacks on Model Aggregation in Federated Learning},
    url={https://arxiv.org/pdf/2006.11901.pdf}
}
@misc{fedma,
    author={H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, Y. Khazaeni},
    title={Federated Learning with Matched Averaging},
    url={https://openreview.net/forum?id=BkluqlSFDS}
}
@article{autofa,
    author={Y. Xia, D. Yang, W. Li, A. Myronenko, D. Xu, H. Obinata, H. Mori, P. An, S. Harmon, E. Turkbey, B. Turkbey, B. Wood, F. Patella, E. Stellato, G. Carrafiello, A. Ierardi, A. Yuille, H. Roth},
    title={Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation},
    url={https://arxiv.org/pdf/2104.10195.pdf},
    year={2021}
}
@article{privamp,
    author={M. S. E. Mohamed, W-T. Chang, R. Tandon,},
    title={Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation},
    url={https://arxiv.org/pdf/2103.01953.pdf},
    year={2021}
}
@article{fedfomo,
    author={M. Zhang, K. Sapra, S. Fidler, S. Yeung, J. M. Alvarez},
    title={Personalized Federated Learning with First Order Model Optimization},
    url={https://arxiv.org/pdf/2012.08565.pdf},
    year={2020}
}
@misc{freerider_defence,
    author={J. Lin, M. Du, J. Liu},
    title={Free-riders in Federated Learning: Attacks and Defenses},
    url={https://arxiv.org/pdf/1911.12560.pdf}
}
@misc{dagmm,
    author={B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, H. Chen},
    title={Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection},
    url={https://openreview.net/forum?id=BJJLHbb0-}
}
@misc{oreily_elbow,
    author={P. Dangeti},
    title={Statistics for Machine Learning},
    url={https://www.oreilly.com/library/view/statistics-for-machine/9781788295758/c71ea970-0f3c-4973-8d3a-b09a7a6553c1.xhtml}
}