A unified framework for privacy-preserving data analysis and machine learning
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Updated
Aug 28, 2024 - Python
A unified framework for privacy-preserving data analysis and machine learning
SRDS 2020: End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning (IEEE MLSP 2022)
reveal the vulnerabilities of SplitNN
Source codes of paper "Can We Use Split Learning on 1D CNN for Privacy Preserving Training?"
Comparison b/w Federated Learning & Split Learning for credit card fraud detection dataset using Pytorch
Official Repository for ResSFL (accepted by CVPR '22)
Enhancing Efficiency in Multidevice Federated Learning through Data Selection
Framework that supports pipeline federated split learning with multiple hops.
Supplementary code for the paper "SplitGuard: Detecting and MitigatingTraining-Hijacking Attacks in Split Learning"
testing adhocSL
Simple Split Learning setup. Proof of Concept & testbed
Split learning for privacy-preserving healthcare, and threats and defensive techniques for decentralized learning. (with Prof. Vinay Chamola)
Official code of the paper "A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning".
Official code for "EC-SNN: Splitting Deep Spiking Neural Networks on Edge Devices" (IJCAI2024)
CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning
Code and data accompanying the DP-FSL paper
Comparison of distributed machine learning techniques applied to openly available datasets
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