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https://github.com/aidecentralized/awesome-split-learning

A curated repository for various papers in the domain of split learning.
https://github.com/aidecentralized/awesome-split-learning

List: awesome-split-learning

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A curated repository for various papers in the domain of split learning.

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# awesome-split-learning

A curated repository for various papers in the domain of split learning.



## Split Training

[Split learning for health: Distributed deep learning without sharing raw patient data](https://arxiv.org/abs/1812.00564)



[SplitFed: When Federated Learning Meets Split Learning](https://arxiv.org/abs/2004.12088)



[Advances and Open Problems in Federated Learning](https://www.nowpublishers.com/article/Details/MAL-083)



[Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare](https://arxiv.org/abs/2012.12591)



[Advancements of federated learning towards privacy preservation: from federated learning to split learning](https://arxiv.org/abs/2011.14818)



[SplitEasy: A Practical Approach for Training ML models on Mobile Devices in a split second](https://arxiv.org/abs/2011.04232)



[FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks](https://arxiv.org/abs/2011.03180)



[Multiple Classification with Split Learning](https://arxiv.org/abs/2008.09874)



[Distributed Heteromodal Split Learning for Vision Aided mmWave Received Power Prediction](https://arxiv.org/abs/2007.08208)



[SplitFed: When Federated Learning Meets Split Learning](https://arxiv.org/abs/2004.12088)



[End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things](https://arxiv.org/abs/2003.13376)



[Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://arxiv.org/abs/2003.12365)



[Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction](https://arxiv.org/abs/2003.00645)



[Split Learning for collaborative deep learning in healthcare](https://arxiv.org/abs/1912.12115)



[ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations](https://arxiv.org/abs/1910.03731)



[Detailed comparison of communication efficiency of split learning and federated learning](https://arxiv.org/abs/1909.09145)



[No Peek: A Survey of private distributed deep learning](https://arxiv.org/abs/1812.03288)



[SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention](https://arxiv.org/pdf/2301.12885.pdf)



[split learning vertically](https://www.google.com/search?q=Ramesh+Raskar+split+learning+2023&client=ubuntu-sn&hs=NYa&channel=fs&sxsrf=AB5stBiYLzBzKnA0mgdfHZ1QQNMkeAb-Xg%3A1688280883102&ei=Mx-hZNr7BfWhptQPzb-PaA&ved=0ahUKEwia4cWyuO__AhX1kIkEHc3fAw0Q4dUDCA4&uact=5&oq=Ramesh+Raskar+split+learning+2023&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCAAQogQyBQgAEKIEOgsIABCKBRCGAxCwAzoFCCEQoAE6BAghEBVKBAhBGAFQ5wFYiQ9g9BVoAXAAeACAAX-IAZQEkgEDMS40mAEAoAEBwAEByAEC&sclient=gws-wiz-serp#fpstate=ive&vld=cid:a617bcc0,vid:pzJ32gK1__M)



[PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients](https://ieeexplore.ieee.org/document/10171521)



## Split Inference



[Interpretable Complex-Valued Neural Networks for Privacy Protection](https://openreview.net/forum?id=S1xFl64tDr)



[Mitigating_Information_Leakage_in_Image_Representations_A_Maximum_Entropy_Approach](https://openaccess.thecvf.com/content_CVPR_2019/papers/Roy_Mitigating_Information_Leakage_in_Image_Representations_A_Maximum_Entropy_Approach_CVPR_2019_paper.pdf)



[NoPeek: Information leakage reduction to share activations in distributed deep learning](https://arxiv.org/abs/2008.09161)



[PRIVATE SPLIT INFERENCE OF DEEP NETWORKS](https://openreview.net/pdf?id=iqmOTi9J7E8)



[DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks](https://arxiv.org/abs/2012.11025)



## surveys



[Decentralized Learning in Healthcare: A Review of Emerging Techniques](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10141615)



## Attacks



[Model Inversion Attacks Against Collaborative Inference](https://dl.acm.org/doi/10.1145/3359789.3359824)



[Unleashing the Tiger: Inference Attacks on Split Learning](https://arxiv.org/abs/2012.02670)



[Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information](https://arxiv.org/pdf/2305.04146.pdf)



## Attacks



[EXACT: Extensive Attack for Split Learning](https://arxiv.org/pdf/2305.12997.pdf)

## Code links

[SplitFed: When Federated Learning Meets Split Learning](https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning)



[Split Neural Networks on PySyft](https://medium.com/analytics-vidhya/split-neural-networks-on-pysyft-ed2abf6385c0)

[PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients](https://github.com/mnswdhw/pfsl)



## courses

[OpenMinded AI concering privacy courses](https://courses.openmined.org/courses/foundations-of-private-computation)